Attributed habitat patches of Idaho

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: McCarley, T. Ryan
Originator: Aycrigg, Jocelyn L.
Publication_Date: 2021
Title: Attributed habitat patches of Idaho
Geospatial_Data_Presentation_Form: vector digital data
Description:
Abstract:
This data publication contains approximately 44.3 million polygons derived from multi-scale object-oriented image analysis attributed with climatic, topographic, soil, vegetation, and disturbance variables. The polygons provide continuous coverage for the entire state of Idaho, USA. Additionally, this publication contains the parameters for lasso logistic regression models generated to predict the probability of plant species occurrence using the variables attributed to each polygon.
Purpose:
To estimate probability of occurrence for 20 ungulate forage species across Idaho and provide a state-wide unit of analysis for other plant species distribution analyses.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2011
Ending_Date: 2015
Currentness_Reference:
Ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent: Idaho, USA
Bounding_Coordinates:
West_Bounding_Coordinate: -117.24332
East_Bounding_Coordinate: -111.04333
North_Bounding_Coordinate: 49.00000
South_Bounding_Coordinate: 42.00000
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Species distribution modelling
Theme_Keyword: Forage species
Theme_Keyword: Object-oriented segmentation
Theme_Keyword: Lasso logistic regression
Theme_Keyword: Idaho, USA
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Categories
Theme_Keyword: biota
Theme_Keyword: climatologyMeterologyAtmosphere
Theme_Keyword: elevation
Theme_Keyword: environment
Theme_Keyword: geoscientificInformation
Theme_Keyword: imageryBaseMapsEarthCover
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Idaho, USA
Taxonomy:
Keywords/Taxon:
Taxonomic_Keyword_Thesaurus: None
Taxonomic_Keywords: multiple species
Taxonomic_Keywords: plants
Taxonomic_Keywords: vegetation
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Pseudoroegneria spicata
Applicable_Common_Name: Bluebunch wheatgrass (PSSP6)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Poa secunda
Applicable_Common_Name: Sandberg bluegrass (POSE)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Festuca idahoensis
Applicable_Common_Name: Idaho fescue (FEIDI2)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Calamagrostis rubscens
Applicable_Common_Name: Pinegrass (CARU)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Carex spp.
Applicable_Common_Name: Sedge spp. (CAREX)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Lupinus spp.
Applicable_Common_Name: Lupine spp. (lupin)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Balsamorhiza sagittata
Applicable_Common_Name: Arrowleaf balsamroot (BASA3)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Achillea millefolium
Applicable_Common_Name: Common yarrow (ACMI2)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Geranium viscosissimum
Applicable_Common_Name: Sticky purple geranium (GEVI2)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Mahonia repens
Applicable_Common_Name: Creeping Oregon grape (MARE11)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Artemisia tridentata ssp. vaseyana
Applicable_Common_Name: Mountain big sagebrush (ARTRV)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Purshia tridentata
Applicable_Common_Name: Antelope bitterbrush (PUTR2)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Symphoricarpos albus
Applicable_Common_Name: Common snowberry (SYAL)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Amelanchier alnifolia
Applicable_Common_Name: Saskatoon seviceberry (AMAL2)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Physocarpus malvaceus
Applicable_Common_Name: Mallow ninebark (PHMA5)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Populus tremuloides
Applicable_Common_Name: Quaking aspen (POTR5)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Prunus virginiana
Applicable_Common_Name: Chokecherry (PRVI)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Pseudotsuga menziesii
Applicable_Common_Name: Douglas-fir (PSME)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Salix spp.
Applicable_Common_Name: Willow spp. (Salix)
Taxon_Rank_Name: Species
Taxon_Rank_Value: Pinus contorta
Applicable_Common_Name: Lodgepole pine (PICO)
Access_Constraints: None
Use_Constraints:
These data were developed for open access and can be used without additional permissions or fees under creative commons license CC BY 4.0. If you use these data in a publication, presentation, or other research product please use the following citation:

McCarley, T. Ryan; Ball, Tara M.; Aycrigg, Jocelyn L.; Strand, Eva K.; Svancara, Leona K.; Horne, Jon S.; Johnson, Tracey N.; Lonneker, Meghan K.; Hurley, Mark. 2020. Predicting fine-scale forage distribution to inform ungulate nutrition. Ecological Informatics 60, 101170. Doi:10.1016/j.ecoinf.2020.101170.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Ryan McCarley
Contact_Organization: University of Idaho
Contact_Position: Research Support Scientist
Contact_Address:
Address_Type: mailing
Address: 875 Perimeter Drive MS-1136
City: Moscow
State_or_Province: Idaho
Postal_Code: 83844
Country: USA
Contact_Voice_Telephone: none provided
Contact_Electronic_Mail_Address: tmccarley@uidaho.edu
Data_Set_Credit:
This project was funded by Idaho Department of Fish and Game through the Pittman Robertson Grant number F16AF00908 as well as the NSF Idaho EPSCoR Program and by the National Science Foundation under award number OIA-1757324.
Cross_Reference:
Citation_Information:
Originator: McCarley, T. Ryan
Originator: Ball, Tara M.
Originator: Aycrigg, Jocelyn L.
Originator: Strand, Eva K.
Originator: Svancara, Leona K.
Originator: Horne, Jon S.
Originator: Johnson, Tracey N.
Originator: Lonneker, Meghan K.
Originator: Hurley, Mark
Publication_Date: 20201001
Title: Predicting fine-scale forage distribution to inform ungulate nutrition
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Ecological Informatics
Issue_Identification: 60, 101170
Online_Linkage: https://doi.org.10.1016/j.ecoinf.2020.101170
Analytical_Tool:
Analytical_Tool_Description:
Software for geospatial analysis
Tool_Access_Information:
Online_Linkage: https://www.esri.com
Tool_Access_Instructions:
Requires license. See website for more details.
Tool_Citation:
Citation_Information:
Originator: Environmental Systems Research Institute
Publication_Date: 2016
Title: ArcGIS Desktop
Edition: 10.4
Geospatial_Data_Presentation_Form: Software
Publication_Information:
Publication_Place: Redlands, CA, USA
Online_Linkage: https://www.esri.com
Analytical_Tool:
Analytical_Tool_Description:
Software for statistical computing
Tool_Access_Information:
Online_Linkage: https://www.R-project.org
Tool_Access_Instructions:
See website for more details.
Tool_Citation:
Citation_Information:
Originator: R Core Team
Publication_Date: 2020
Title: R: A language and environment for statistical computing
Geospatial_Data_Presentation_Form: Software
Publication_Information:
Publication_Place: Vienna, Austria
Online_Linkage: https://www.R-project.org
Analytical_Tool:
Analytical_Tool_Description:
Software for multi-level object-oriented imagery segmentation
Tool_Access_Information:
Online_Linkage: http://www.trimble.com
Tool_Access_Instructions:
Requires license. See website for more details.
Tool_Citation:
Citation_Information:
Originator: Trimble Inc.
Publication_Date: 2016
Title: eCognition Developer
Edition: 9.2
Geospatial_Data_Presentation_Form: Software
Publication_Information:
Publication_Place: Westminster, CO, USA
Online_Linkage: https://www.trimble.com
Back to Top
Data_Quality_Information:
Logical_Consistency_Report:
All data were projected into NAD83 Idaho Transverse Mercator. All polygons were checked for and cleared of topology overlap errors.
Completeness_Report:
In general, the data provides wall-to-wall coverage of Idaho, USA. However, we have observed that a few polygons are missing from the dataset. Such omissions appear to be correlated with large, homogenous, and non-vegetated surfaces such as roads and lava flows. The exact number of polygon omissions is unknown.

Most polygons are attributed with mean and standard deviation of the four NAIP bands (blue, green, red, and near-infrared). However, some NA's were generated when fixing topology overlap errors. The exact number of polygons affected is unknown.

All polygons touching the Idaho border were included in the data. However, many environmental variables were clipped at the Idaho border, so environmental attributes may be representative only of the portion of the polygon within Idaho.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Bureau of Land Management (BLM)
Publication_Date: Unknown
Title: Vegetation survey data
Geospatial_Data_Presentation_Form: database
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2012
Ending_Date: 2016
Source_Citation_Abbreviation:
BLM vegetation data
Source_Contribution:
Line point intercept data sampled on 50 or 100m transects at every 0.5 or 1m.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Idaho Department of Fish and Game (IDFG)
Publication_Date: Unknown
Title: Vegetation survey data
Geospatial_Data_Presentation_Form: database
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2012
Ending_Date: 2016
Source_Citation_Abbreviation:
IDFG vegetation data
Source_Contribution:
Line point intercept data sampled on 50 or 100m transects at every 0.5 or 1m.
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Agriculture Imagery Program (NAIP)
Publication_Date: Unknown
Title: Imagery
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: http://www.insideidaho.org
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 2011
Single_Date/Time:
Calendar_Date: 2015
Source_Citation_Abbreviation:
NAIP imagery
Source_Contribution:
NAIP 4 band (blue, green, red, near-infrared) imagery (1m resolution) from 2015 was used as an input for object-oriented segmentation. Imagery from 2011 was substituted in a few instances where areas were obscured by snow or clouds.
Source_Information:
Source_Citation:
Citation_Information:
Originator: United States Geological Survey
Publication_Date: Unknown
Title: Digital Elevation Model
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: https://www.insideidaho,org
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 1999
Source_Citation_Abbreviation:
USGS elevation data
Source_Contribution:
Elevation data (10m resolution) were downloaded from insideidaho.org for all of Idaho.
Source_Information:
Source_Citation:
Citation_Information:
Originator: PRISM Climate Group
Publication_Date: Unknown
Title: Gridded Climate Data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Corvallis, OR, USA
Publisher: Oregon State University
Online_Linkage: https://prism.oregonstate.edu
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1981
Ending_Date: 2010
Source_Citation_Abbreviation:
PRISM climate data
Source_Contribution:
Downloaded 30-year average maximum temperature, maximum precipitation, minimum temperature, minimum precipitation, and annual precipitation.
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Resource Conservation Service
Publication_Date: Unknown
Title: Soil Data
Geospatial_Data_Presentation_Form: vector digital data
Online_Linkage: https:/www.nrcs.usda.gov
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1901
Ending_Date: 2015
Source_Citation_Abbreviation:
NRCS soil data
Source_Contribution:
Downloaded cation-exchange capacity 0-25cm, percent clay 0-25cm, percent sand 0-25cm, percent silt 0-25cm, pH 0-25cm, available water supply 0-25cm, depth to any soil restrictive layer, percent calcium carbonate 0-25cm, and percent organic matter 0-25cm.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Eidenshink, J.
Originator: Schwind, B.
Originator: Brewer, K.
Originator: Zhu, Z.
Originator: Quayle, B.
Originator: Howard, S.
Publication_Date: 2007
Title: A project for monitoring trends in burn severity.
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Fire Ecology
Issue_Identification: 3, 3-21
Online_Linkage: http://www.mtbs.gov
Type_of_Source_Media: vector digital data
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1984
Ending_Date: 2014
Source_Citation_Abbreviation:
MTBS fire data
Source_Contribution:
Downloaded all fire perimeters for fires in Idaho greater than 1000 acres.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Rollins, Matthew C.
Publication_Date: 2009
Title: LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: International Journal of Wildland Fire
Issue_Identification: 18:3, 235-249
Online_Linkage: https://www.landfire.gov
Type_of_Source_Media: raster digital data
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2011
Source_Citation_Abbreviation:
LANDFIRE shrub cover
Source_Contribution:
Downloaded percent canopy cover of shrubs (30m resolution). Shrub cover is in 10% increments.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Holmer, C.
Originator: Dewitz, J.
Originator: Yang, L.
Originator: Jin, S.
Originator: Danielson, P.
Originator: Xian, G.
Originator: Coulston, J.
Originator: Herold, N.
Originator: Wickham, J.
Originator: Megown, K.
Publication_Date: 2015
Title: Completion of the 2011 National Land Cover Database for the conterminous United States - representing a decade of land cover change information
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Photogrammetric Engineering and Remote Sensing
Online_Linkage: https:/www.mrlc.gov
Type_of_Source_Media: raster digital data
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2011
Source_Citation_Abbreviation:
NLCD land cover data
Source_Contribution:
Downloaded percent tree canopy cover (30m resolution). Tree cover is an integer value between 0 and 100. Also downloaded land cover to identify developed areas.
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA National Agricultural Statistics Service
Publication_Date: Unknown
Title: Land Cover Data
Geospatial_Data_Presentation_Form: raster digital data
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2014
Source_Citation_Abbreviation:
NASS land cover data
Source_Contribution:
Downloaded land cover data (30m resolution) to identify and omit agricultural areas, barren land, and perennial snow and ice.< /dd>
Process_Step:
Process_Description:
NAIP imagery was segmented in eCognition Developer 9.2 using a shape value of 0.1 and a compactness value of 0.3. Processing was across Idaho done by USGS 1:100,000 quadrangles. Edge effects between quadrangles were eliminated by selecting segmented polygons with edge effects and re-segmenting the NAIP imagery for those areas to produce continuous segmented polygons across the state, free from artificial lines caused by the 1:100,000 quadrangles. For the sake of memory constraints and processing efficiency, segmented polygons were grouped by USGS 1:24,000 quadrangles across the state.

The resulting polygons contained attributes for mean and standard deviation of each NAIP band.
Source_Used_Citation_Abbreviation:
NAIP imagery
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
Segmented polygons
mean_blue
mean_green
mean_red
mean_nir
sd_blue
sd_green
sd_red
sd_nir
Process_Step:
Process_Description:
Multiple variables were generated from the USGS elevation data in ArcGIS Desktop 10.4. These included elevation (ele), slope (slp), sine of aspect (sasp), cosine of aspect (casp), landscape curvature index (lcv), topographic position index (tpi), topographic wetness index (twi), and solar radiation index (sri).

Elevation was derived directly from the USGS DEM. Slope was calculated using the ArcGIS slope function.

Sine of aspect was calculated using the aspect function in ArcGIS, reclassifying flat areas to NoData, converting to radians, applying sine, multiplying by 10, and converting to integer.

Cosine of aspect was calculated using the aspect function in ArcGIS, reclassifying flat areas to NoData, converting to radians, applying cosine, multiplying by 10, and converting to integer.

Landscape curvature index was calculated using the curvature function in ArcGIS with the standard curvature type and a Z factor of 1.

Topographic position index was calculated in the Land Facet Corridor Designer ArcGIS tool extension using a 'circle' neighborhood shape, with a 21.5m radius, 'cells' as the neighborhood size unit, and leaving the 'setting no data if any no data cells are in the neighborhood' unchecked.

The topographic position index tool is described in:
Jenness, J., B. Brost, and P. Beier. 2013. Land Facet Corridor Designer. https://corridordesign.org

Topographic wetness index was calculated in ArcGIS using the equation: twi = ln(Sca_scaled/Tan_slp), where Fd = flow direction from the ArcGIS flow direction tool in Spatial Analyst
Sca = flow accumulation from the ArcGIS flow accumulation tool in Spatial Analyst (requires Fd)
Slope = slope from the USGS DEM converter to radians
Sca_scaled = (Sca+1)*10
Tan_slp = Con(Slope>0, tan(Slope), 0.001)

Topographic wetness index is defined in:
Moore, I.D., Gessler, P.E., Nielsen, G.A., Petersen, G.A., 1993. Terrain attributes: Estimation methods and scale effects. In: Jakeman, A.J., Beck, M.B., McAleer, M. (Eds.) Modelling Change in Environmental Systems. Wiley, London, pp. 189-214.

Solar radiation index was calculated using the solar radiation function in ArcGIS. The DEM was first divided into 1 degree latitudes. For each latitudinal strip (i.e, 42, 43, 44, etc.), tool inputs were the latitude being processing, '200' for sky size, 'multiple days in a year' for time configuration, '2015' for year, '15 May 2015' for start date, and '31 August 2015' for end date. Output rasters of each latitude were merged to provide a state-wide solar radiation index raster.
Source_Used_Citation_Abbreviation:
USGS elevation data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
ele
slp
sasp
casp
lcv
tpi
twi
sri
Process_Step:
Process_Description:
The 30-year average climate variables obtained from PRISM were resampled from either 800m (precipitation) or 4km (temperature) to 250m using an algorithm developed by Holden et al. 2011. Results were labelled accordingly: maximum temperature = maxtp, minimum temperature = mintp, maximum precipitation = maxpr, minimum precipitation = minpr, annual precipitation = tapr.

Holden, Z.A., Abatzoglou, J.T., Luce, C.H., Bagget, L.S., 2011. Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain. Agricultural and Forestry Meteorology. 151:8, 1066-1073. https://doi,org/10.1016/j.agrformet.2011.03.011.
Source_Used_Citation_Abbreviation:
PRISM climate data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
maxtp
maxpr
mintp
minpr
tapr
Process_Step:
Process_Description:
NRCS soil data was compiled from both the Soil Survey Geographic database (SSURGO) and the Digital General Soil map (STATSGO2). SSURGO and STATSGO2 vector data were converted into raster grids and combined using 'Mosaic to new raster" in ArcGIS, with an overlap priority setting for SSURGO data. This process was conducted for cation-exchange capacity (cec), percent clay (clay), percent sand (sand), percent silt (silt), pH (ph), available water supply (aws), depth to any restrictive layer (d2r), percent calcium carbonate (caco3), and percent organic matter (om).

Leona Svancara at the Idaho Department of Fish and Game is credited for compiling and processing this data.
Source_Used_Citation_Abbreviation:
NRCS soil data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
cec
clay
sand
silt
ph
aws
d2r
caco3
om
Process_Step:
Process_Description:
MTBS fire perimeters were gridded into 30m rasters then overlaid to determine the number of fires between 1984 and 2014 (fire frequency; ff) and the number of years since the most recent fire (time since fire; tsf).

Tara Ball at the Idaho Department of Fish and game is credited with compiling and processing this data.
Source_Used_Citation_Abbreviation:
MTBS fire data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
ff
tsf
Process_Step:
Process_Description:
Downloaded shrub cover data was reclassified to integers between 0 and 9, such that:
1 - >=10% & <20%
2 - >=20% & <30%
3 - >=30% & <40%
4 - >=40% & <50%
5 - >=50% & <60%
6 - >=60% & <70%
7 - >=70% & <80%
8 - >=80% & <90%
9 - >=90% & <=100%
0 - all other classes
Source_Used_Citation_Abbreviation:
LANDFIRE shrub cover
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
sc
Process_Step:
Process_Description:
NLCD tree cover (tc) was used as is, with integer values between 0 and 100 representing percent tree cover.

NLCD land cover classes (dev) were used as a mask to later exclude non-natural vegetation and non-vegetation. Notable classes include:
0 - Unclassified
11 - Open Water
12 - Perennial Snow/Ice
21 through 24 - Developed (all intensities)
31 - Barren Land
82 - Cultivated Crops
All class codes can be found at https://www.mrlc.gov/nlcd06_leg.php
Source_Used_Citation_Abbreviation:
NLCD land cover data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
tc
dev
Process_Step:
Process_Description:
NASS data (nass) was used as a mask to later exclude non-natural vegetation and non-vegetation. Notable classes include:
0 - Background
1 through 77 - Agriculture (specific crops)
111 - Open Water
112 - Perennial Ice/Snow
121 through 124 - Developed (all intensities)
131 - Barren
205 through 247 - Agriculture (specific crops)
All class codes can be found at https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/2015_cultivated_layer_metadata.php
Source_Used_Citation_Abbreviation:
NASS land cover data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
nass
Process_Step:
Process_Description:
Each environmental variable was summarized for each polygon using the Zonal statistics as table tool in ArcGIS (ran using the arcpy python module) on each 1:24,000 quadrangle. Continuous variables (ele, sri, maxtp, maxpr, mintp, minpr, tapr, cec, clay, sand, silt, tc, aws, d2r, caco3, and om) were summarized by their mean, while index values (sasp, casp, lcv, tpi, twi, ph, sc, ff, and tsf) were summarized by their mode. The tables were then joined to the segmented polygons.

Additional attributes added to the segmented polygons were 'long', 'lat', 'quad', 'id', and 'shp_area'. Latitude (lat) and longitude (long) were computed in R for each polygon center. Area in square meters of the polygon (shp_area) was calculated in R. Quad indicated which 1:24,000 quadrangle the polygon was mostly contained by. Id is a unique identifier for each polygon in a given quad.

The result was the final set of Idaho habitat patches, indexed by 1:24,000 quadrangle.
Source_Used_Citation_Abbreviation:
Segmented polygons
ele
slp
casp
sasp
twi
lcv
sri
tpi
minpr
maxpr
aws
clay
sand
silt
cec
d2r
ph
om
caco3
tsf
ff
tc
sc
nass
dev
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
Idaho habitat patches
id
quad
long
lat
shp_area
Process_Step:
Process_Description:
Vegetation survey data were compiled in Microsoft Access to ensure consistency between data sources and naming conventions. If exact coordinates for each line point were not included in the original data, they were calculated in R based on direction, azimuth, and starting coordinates of the transect and added to the Access database.

Once the data was cleaned, .csv tables exported from access were joined with the Idaho habitat patches shapefiles using spatial join in ArcGIS. Using R, the results of the join were summarized to include the number of observations of each target species and the number of total line points for each habitat patch (i.e., polygon), along with the environmental attributes already contained in the Idaho habitat patches shapefiles.

Lasso logistic regression was conducted in R, using the 'glmnet' library. Models were run for 20 forage species using a set of all environmental predictors (distal-proximal) and variables with proximal effects only. Proximal refers to a variable having more direct impact on plant growth and distal, less direct impact. Each model was saved as an .rds file, which can be used to make occurrence predictions for the 20 forage species across any of the Idaho habitat patch shapefiles.

For more details on modelling, see the cross-referenced article, McCarley et al. 2020.
Source_Used_Citation_Abbreviation:
Idaho habitat patches
BLM vegetation data
IDFG vegetation data
Process_Date: Unknown
Source_Produced_Citation_Abbreviation:
Plant species models
Back to Top
Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Vector
Back to Top
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name: Universal Transverse Mercator
Universal_Transverse_Mercator:
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.9996
Longitude_of_Central_Meridian: -114.0
Latitude_of_Projection_Origin: 42.0
False_Easting: 2500000.0
False_Northing: 1200000.0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: Coordinate Pair
Coordinate_Representation:
Abscissa_Resolution: 0.00000000222002416450096
Ordinate_Resolution: 0.00000000222002416450096
Planar_Distance_Units: Meters
Geodetic_Model:
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.257222101
Back to Top
Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: IdahoPolygons
Entity_Type_Definition:
Segmented polygons (habitat patches) attributed with environmental variables.
Entity_Type_Definition_Source:
Derived from object-oriented segmentation of 2011 and 2015 National Agriculture Imagery Program (NAIP) imagery and attributed with other data sources.
Attribute:
Unique polygon identifier
Attribute_Label:id
Attribute:
Mean blue NAIP reflectance
Attribute_Label:mean_blue
Attribute:
Mean green NAIP reflectance
Attribute_Label:mean_green
Attribute:
Mean red NAIP reflectance
Attribute_Label:mean_red
Attribute:
Mean near-infrared NAIP reflectance
Attribute_Label:mean_nir
Attribute:
Standard deviation blue NAIP reflectance
Attribute_Label:sd_blue
Attribute:
Standard deviation green NAIP reflectance
Attribute_Label:sd_green
Attribute:
Standard deviation red NAIP reflectance
Attribute_Label:sd_red
Attribute:
Standard deviation near-infrared NAIP reflectance
Attribute_Label:sd_nir
Attribute:
USGS 1:24,000 quadrangle unique identifier, prefixed with 'q'
Attribute_Label:quad
Attribute:
Elevation in meters
Attribute_Label:ele
Attribute:
Slope in degrees
Attribute_Label:slp
Attribute:
Cosine aspect, describes how north (10) or south facing (-10) the slope is, with 0.001 representing flat areas
Attribute_Label:casp
Attribute:
Sine aspect, describes how east (10) or west facing (-10) the slope is, with 0.001 representing flat areas
Attribute_Label:sasp
Attribute:
Topographic wetness index, classifying hilltops, valley bottoms, exposed ridges, flat plains, and upper or lower slope
Attribute_Label:twi
Attribute:
Landscape curvature index, describes upwardly convex (+ value), upwardly concave (- value), and flat (0)
Attribute_Label:lcv
Attribute:
Solar radiation index, insolation (WH/m2) during main growing season
Attribute_Label:sri
Attribute:
Topographic slope position index, describing position higher than surrounding (+ value), lower than surrounding (- value), or similar (0)
Attribute_Label:tpi
Attribute:
30-year average minimum precipitation (mm)
Attribute_Label:minpr
Attribute:
30-year average maximum precipitation (mm)
Attribute_Label:maxpr
Attribute:
30-year average annual precipitation (mm)
Attribute_Label:tapr
Attribute:
30-year average minimum temperature (deg C)
Attribute_Label:mintp
Attribute:
30-year average maximum temperature (deg C)
Attribute_Label:maxtp
Attribute:
Available water supply (cm) from surface to 25cm depth
Attribute_Label:aws
Attribute:
Percent clay from surface to 25cm depth
Attribute_Label:clay
Attribute:
Percent sand from surface to 25cm depth
Attribute_Label:sand
Attribute:
Percent silt from surface to 25cm depth
Attribute_Label:silt
Attribute:
Cation exchange capacity (CEC-7; milliequivalents/100g) from surface to 25cm depth
Attribute_Label:cec
Attribute:
Depth to any restrictive layer (cm)
Attribute_Label:d2r
Attribute:
pH from surface to 25cm depth
Attribute_Label:ph
Attribute:
Percent organic matter from surface to 25cm depth
Attribute:
Percent calcium carbonate from surface to 25cm depth
Attribute_Label:caco3
Attribute:
Time since most recent fire (years), only going back to 1984 (31 years)
Attribute_Label:tsf
Attribute:
Fire frequency (number of fires), only going back to 1984
Attribute_Label:ff
Attribute:
Percent tree cover (to nearest 1%)
Attribute_Label:tc
Attribute:
Percent shrub cover (indexed to 10% increments)
Attribute_Label:sc
Attribute:
USDA National Agricultural Statistics Service land cover data (2014)
Attribute_Label:nass
Attribute:
National Land Cover Database land cover data (2011)
Attribute_Label:dev
Attribute:
Longitude of polygon center (in native UTM coordinates)
Attribute_Label:long
Attribute:
Latitude of polygon center (in native UTM coordinates)
Attribute_Label:lat
Attribute:
Area (m2) of polygon
Attribute_Label:shp_area
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find the data available in this archive and a short description of it's contents.

DATA FILES

\Data\IdahoPolygons\*.zip: Shapefiles of the Idaho Habitat Patches organized by USGS 1:24,000 quadrangles unique identifier "UID" prefixed with the letter "q".
\Data\SDMs\*.zip: R Data Files containing the lasso logistic regression models organize by USDA species code.

SUPPLEMENTAL FILES
\Supplamental\IdahoPolygons\USGS24k.shp: Shapefile of USGS 1:24,000 quadrangles in Idaho.
\Supplamental\SDMs\applyModel.R: R script/function for applying species distribution models to the habitat patches.
Entity_and_Attribute_Detail_Citation:
none provided
Back to Top
Metadata_Reference_Information:
Metadata_Date: 20210126
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Ryan McCarley
Contact_Organization: University of Idaho
Contact_Position: Reseach Support Scientist
Contact_Address:
Address_Type: mailing
Address: MS 1133 875 Perimeter Dr
City: Moscow
State_or_Province: Idaho
Postal_Code: 83844
Contact_Voice_Telephone: none provided
Contact_Electronic_Mail_Address: tmccarley@uidaho.edu
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
Back to Top