FGDC Metadata as XML
<?xml-stylesheet '' type='text/xsl'?>
<metadata>
<idinfo>
<citation>
<citeinfo>
<origin>McCarley, T. Ryan</origin>
<origin>Aycrigg, Jocelyn L.</origin>
<pubdate>2021</pubdate>
<title>Fine-scale habitat patches of Idaho attributed with climatic, topographic, soil, vegetation, and disturbance variables</title>
<geoform>vector digital data</geoform>
</citeinfo>
</citation>
<descript>
<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.</abstract>
<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.</purpose>
</descript>
<timeperd>
<timeinfo>
<rngdates>
<begdate>2011</begdate>
<enddate>2015</enddate>
</rngdates>
</timeinfo>
<current>Ground condition</current>
</timeperd>
<status>
<update>None planned</update>
<progress>Complete</progress>
</status>
<spdom>
<bounding>
<eastbc>-111.04333</eastbc>
<northbc>49.00000</northbc>
<westbc>-117.24332</westbc>
<southbc>42.00000</southbc>
</bounding>
<descgeog>Idaho, USA</descgeog>
</spdom>
<keywords>
<theme>
<themekt>None</themekt>
<themekey>Species distribution modelling</themekey>
<themekey>Forage species</themekey>
<themekey>Object-oriented segmentation</themekey>
<themekey>Lasso logistic regression</themekey>
<themekey>Idaho, USA</themekey>
</theme>
<theme>
<themekt>ISO 19115 Topic Categories</themekt>
<themekey>biota</themekey>
<themekey>climatologyMeterologyAtmosphere</themekey>
<themekey>elevation</themekey>
<themekey>environment</themekey>
<themekey>geoscientificInformation</themekey>
<themekey>imageryBaseMapsEarthCover</themekey>
</theme>
<place>
<placekt>None</placekt>
<placekey>Idaho, USA</placekey>
</place>
</keywords>
<accconst>None</accconst>
<taxonomy>
<keywtax>
<taxonkt>None</taxonkt>
<taxonkey>multiple species</taxonkey>
<taxonkey>plants</taxonkey>
<taxonkey>vegetation</taxonkey>
</keywtax>
<taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Pseudoroegneria spicata</taxonrv>
<common>Bluebunch wheatgrass (PSSP6)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Poa secunda</taxonrv>
<common>Sandberg bluegrass (POSE)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Festuca idahoensis</taxonrv>
<common>Idaho fescue (FEIDI2)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Calamagrostis rubscens</taxonrv>
<common>Pinegrass (CARU)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Carex spp.</taxonrv>
<common>Sedge spp. (CAREX)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Lupinus spp.</taxonrv>
<common>Lupine spp. (lupin)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Balsamorhiza sagittata</taxonrv>
<common>Arrowleaf balsamroot (BASA3)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Achillea millefolium</taxonrv>
<common>Common yarrow (ACMI2)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Geranium viscosissimum</taxonrv>
<common>Sticky purple geranium (GEVI2)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Mahonia repens</taxonrv>
<common>Creeping Oregon grape (MARE11)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Artemisia tridentata ssp. vaseyana</taxonrv>
<common>Mountain big sagebrush (ARTRV)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Purshia tridentata</taxonrv>
<common>Antelope bitterbrush (PUTR2)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Symphoricarpos albus</taxonrv>
<common>Common snowberry (SYAL)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Amelanchier alnifolia</taxonrv>
<common>Saskatoon seviceberry (AMAL2)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Physocarpus malvaceus</taxonrv>
<common>Mallow ninebark (PHMA5)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Populus tremuloides</taxonrv>
<common>Quaking aspen (POTR5)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Prunus virginiana</taxonrv>
<common>Chokecherry (PRVI)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Pseudotsuga menziesii</taxonrv>
<common>Douglas-fir (PSME)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Salix spp.</taxonrv>
<common>Willow spp. (Salix)</common>
</taxoncl>
<taxoncl>
<taxonrn>Species</taxonrn>
<taxonrv>Pinus contorta</taxonrv>
<common>Lodgepole pine (PICO)</common>
</taxoncl>
<taxonrn>Kingdom</taxonrn>
<taxonrv>Plantae</taxonrv>
</taxoncl>
</taxonomy>
<useconst>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:<br /><br />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.</useconst>
<ptcontac>
<cntinfo>
<cntperp>
<cntper>Ryan McCarley</cntper>
<cntorg>University of Idaho</cntorg>
</cntperp>
<cntaddr>
<addrtype>mailing</addrtype>
<address>875 Perimeter Drive MS-1136</address>
<city>Moscow</city>
<state>Idaho</state>
<postal>83844</postal>
<country>USA</country>
</cntaddr>
<cntvoice>none provided</cntvoice>
<cntpos>Research Support Scientist</cntpos>
<cntemail>tmccarley@uidaho.edu</cntemail>
</cntinfo>
</ptcontac>
<crossref>
<citeinfo>
<origin>McCarley, T. Ryan</origin>
<origin>Ball, Tara M.</origin>
<origin>Aycrigg, Jocelyn L.</origin>
<origin>Strand, Eva K.</origin>
<origin>Svancara, Leona K.</origin>
<origin>Horne, Jon S.</origin>
<origin>Johnson, Tracey N.</origin>
<origin>Lonneker, Meghan K.</origin>
<origin>Hurley, Mark</origin>
<pubdate>20201001</pubdate>
<title>Predicting fine-scale forage distribution to inform ungulate nutrition</title>
<geoform>journal article</geoform>
<serinfo>
<sername>Ecological Informatics</sername>
<issue>60, 101170</issue>
</serinfo>
<onlink>https://doi.org.10.1016/j.ecoinf.2020.101170</onlink>
</citeinfo>
</crossref>
<tool>
<tooldesc>Software for geospatial analysis</tooldesc>
<toolacc>
<onlink>https://www.esri.com</onlink>
<toolinst>Requires license. See website for more details.</toolinst>
</toolacc>
<toolcite>
<citeinfo>
<origin>Environmental Systems Research Institute</origin>
<pubdate>2016</pubdate>
<title>ArcGIS Desktop</title>
<edition>10.4</edition>
<geoform>Software</geoform>
<pubinfo>
<pubplace>Redlands, CA, USA</pubplace>
</pubinfo>
<onlink>https://www.esri.com</onlink>
</citeinfo>
</toolcite>
</tool>
<tool>
<tooldesc>Software for statistical computing</tooldesc>
<toolacc>
<onlink>https://www.R-project.org</onlink>
<toolinst>See website for more details.</toolinst>
</toolacc>
<toolcite>
<citeinfo>
<origin>R Core Team</origin>
<pubdate>2020</pubdate>
<title>R: A language and environment for statistical computing</title>
<geoform>Software</geoform>
<pubinfo>
<pubplace>Vienna, Austria</pubplace>
</pubinfo>
<onlink>https://www.R-project.org</onlink>
</citeinfo>
</toolcite>
</tool>
<datacred>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.</datacred>
<tool>
<tooldesc>Software for multi-level object-oriented imagery segmentation</tooldesc>
<toolcite>
<citeinfo>
<origin>Trimble Inc.</origin>
<pubdate>2016</pubdate>
<title>eCognition Developer</title>
<edition>9.2</edition>
<geoform>Software</geoform>
<pubinfo>
<pubplace>Westminster, CO, USA</pubplace>
</pubinfo>
<onlink>https://www.trimble.com</onlink>
</citeinfo>
</toolcite>
<toolacc>
<onlink>http://www.trimble.com</onlink>
<toolinst>Requires license. See website for more details.</toolinst>
</toolacc>
</tool>
</idinfo>
<dataqual>
<logic>All data were projected into NAD83 Idaho Transverse Mercator. All polygons were checked for and cleared of topology overlap errors.</logic>
<complete>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.<br /><br />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.<br /><br />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.</complete>
<lineage>
<srcinfo>
<srccite>
<citeinfo>
<origin>Bureau of Land Management (BLM)</origin>
<pubdate>Unknown</pubdate>
<title>Vegetation survey data</title>
<geoform>database</geoform>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<rngdates>
<begdate>2012</begdate>
<enddate>2016</enddate>
</rngdates>
</timeinfo>
</srctime>
<srccitea>BLM vegetation data</srccitea>
<srccontr>Line point intercept data sampled on 50 or 100m transects at every 0.5 or 1m.</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>Idaho Department of Fish and Game (IDFG)</origin>
<pubdate>Unknown</pubdate>
<title>Vegetation survey data</title>
<geoform>database</geoform>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<rngdates>
<begdate>2012</begdate>
<enddate>2016</enddate>
</rngdates>
</timeinfo>
</srctime>
<srccitea>IDFG vegetation data</srccitea>
<srccontr>Line point intercept data sampled on 50 or 100m transects at every 0.5 or 1m.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>National Agriculture Imagery Program (NAIP)</origin>
<pubdate>Unknown</pubdate>
<title>Imagery</title>
<geoform>raster digital data</geoform>
<onlink>http://www.insideidaho.org</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<mdattim>
<sngdate>
<caldate>2011</caldate>
</sngdate>
<sngdate>
<caldate>2015</caldate>
</sngdate>
</mdattim>
</timeinfo>
</srctime>
<srccitea>NAIP imagery</srccitea>
<srccontr>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.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>United States Geological Survey</origin>
<pubdate>Unknown</pubdate>
<title>Digital Elevation Model</title>
<geoform>raster digital data</geoform>
<onlink>https://www.insideidaho,org</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<sngdate>
<caldate>1999</caldate>
</sngdate>
</timeinfo>
</srctime>
<srccitea>USGS elevation data</srccitea>
<srccontr>Elevation data (10m resolution) were downloaded from insideidaho.org for all of Idaho.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>PRISM Climate Group</origin>
<pubdate>Unknown</pubdate>
<title>Gridded Climate Data</title>
<geoform>raster digital data</geoform>
<pubinfo>
<pubplace>Corvallis, OR, USA</pubplace>
<publish>Oregon State University</publish>
</pubinfo>
<onlink>https://prism.oregonstate.edu</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<rngdates>
<begdate>1981</begdate>
<enddate>2010</enddate>
</rngdates>
</timeinfo>
</srctime>
<srccitea>PRISM climate data</srccitea>
<srccontr>Downloaded 30-year average maximum temperature, maximum precipitation, minimum temperature, minimum precipitation, and annual precipitation.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>National Resource Conservation Service</origin>
<pubdate>Unknown</pubdate>
<title>Soil Data</title>
<geoform>vector digital data</geoform>
<onlink>https:/www.nrcs.usda.gov</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<rngdates>
<begdate>1901</begdate>
<enddate>2015</enddate>
</rngdates>
</timeinfo>
</srctime>
<srccitea>NRCS soil data</srccitea>
<srccontr>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.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>Eidenshink, J.</origin>
<origin>Schwind, B.</origin>
<origin>Brewer, K.</origin>
<origin>Zhu, Z.</origin>
<origin>Quayle, B.</origin>
<origin>Howard, S.</origin>
<pubdate>2007</pubdate>
<title>A project for monitoring trends in burn severity.</title>
<geoform>journal article</geoform>
<serinfo>
<sername>Fire Ecology</sername>
<issue>3, 3-21</issue>
</serinfo>
<onlink>http://www.mtbs.gov</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<rngdates>
<begdate>1984</begdate>
<enddate>2014</enddate>
</rngdates>
</timeinfo>
</srctime>
<srccitea>MTBS fire data</srccitea>
<typesrc>vector digital data</typesrc>
<srccontr>Downloaded all fire perimeters for fires in Idaho greater than 1000 acres.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>Rollins, Matthew C.</origin>
<pubdate>2009</pubdate>
<title>LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment</title>
<geoform>journal article</geoform>
<serinfo>
<sername>International Journal of Wildland Fire</sername>
<issue>18:3, 235-249</issue>
</serinfo>
<onlink>https://www.landfire.gov</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<sngdate>
<caldate>2011</caldate>
</sngdate>
</timeinfo>
</srctime>
<srccitea>LANDFIRE shrub cover</srccitea>
<typesrc>raster digital data</typesrc>
<srccontr>Downloaded percent canopy cover of shrubs (30m resolution). Shrub cover is in 10% increments,
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>Holmer, C.</origin>
<origin>Dewitz, J.</origin>
<origin>Yang, L.</origin>
<origin>Jin, S.</origin>
<origin>Danielson, P.</origin>
<origin>Xian, G.</origin>
<origin>Coulston, J.</origin>
<origin>Herold, N.</origin>
<origin>Wickham, J.</origin>
<origin>Megown, K.</origin>
<pubdate>2015</pubdate>
<title>Completion of the 2011 National Land Cover Database for the conterminous United States - representing a decade of land cover change information</title>
<geoform>journal article</geoform>
<serinfo>
<sername>Photogrammetric Engineering and Remote Sensing</sername>
</serinfo>
<onlink>https:/www.mrlc.gov</onlink>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<sngdate>
<caldate>2011</caldate>
</sngdate>
</timeinfo>
</srctime>
<srccitea>NLCD land cover data</srccitea>
<typesrc>raster digital data</typesrc>
<srccontr>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.
</srccontr>
</srcinfo>
<srcinfo>
<srccite>
<citeinfo>
<origin>USDA National Agricultural Statistics Service</origin>
<pubdate>Unknown</pubdate>
<title>Land Cover Data</title>
<geoform>raster digital data</geoform>
</citeinfo>
</srccite>
<srctime>
<timeinfo>
<sngdate>
<caldate>2014</caldate>
</sngdate>
</timeinfo>
</srctime>
<srccitea>NASS land cover data</srccitea>
<srccontr>Downloaded land cover data (30m resolution) to identify and omit agricultural areas, barren land, and perennial snow and ice.
</srccontr>
</srcinfo>
<procstep>
<srcused>NAIP imagery</srcused>
<procdesc>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.<br /><br />The resulting polygons contained attributes for mean and standard deviation of each NAIP band.</procdesc>
<procdate>Unknown</procdate>
<srcprod>Segmented polygons<br />mean_blue<br />mean_green<br />mean_red<br />mean_nir<br />sd_blue<br />sd_green<br />sd_red<br />sd_nir</srcprod>
</procstep>
<procstep>
<srcused>USGS elevation data</srcused>
<procdesc>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).<br /><br />Elevation was derived directly from the USGS DEM. Slope was calculated using the ArcGIS slope function.<br /><br />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.<br /><br />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.<br /><br />Landscape curvature index was calculated using the curvature function in ArcGIS with the standard curvature type and a Z factor of 1.<br /><br />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.<br /><br />The topographic position index tool is described in:<br />Jenness, J., B. Brost, and P. Beier. 2013. Land Facet Corridor Designer. https://corridordesign.org<br /><br />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<br />Sca = flow accumulation from the ArcGIS flow accumulation tool in Spatial Analyst (requires Fd)<br />Slope = slope from the USGS DEM converter to radians<br />Sca_scaled = (Sca+1)*10<br />Tan_slp = Con(Slope>0, tan(Slope), 0.001)<br /><br />Topographic wetness index is defined in:<br />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.<br /><br />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..</procdesc>
<procdate>Unknown</procdate>
<srcprod>ele<br />slp<br />sasp<br />casp<br />lcv<br />tpi<br />twi<br />sri</srcprod>
</procstep>
<procstep>
<srcused>PRISM climate data</srcused>
<procdesc>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.<br /><br />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.</procdesc>
<procdate>Unknown</procdate>
<srcprod>maxtp<br />maxpr<br />mintp<br />minpr<br />tapr</srcprod>
</procstep>
<procstep>
<srcused>NRCS soil data</srcused>
<procdesc>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).<br /><br />Leona Svancara at the Idaho Department of Fish and Game is credited for compiling and processing this data.</procdesc>
<procdate>Unknown</procdate>
<srcprod>cec<br />clay<br />sand<br />silt<br />ph<br />aws<br />d2r<br />caco3<br />om</srcprod>
</procstep>
<procstep>
<srcused>MTBS fire data</srcused>
<procdesc>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).<br /><br />Tara Ball at the Idaho Department of Fish and game is credited with compiling and processing this data.</procdesc>
<procdate>Unknown</procdate>
<srcprod>ff<br />tsf</srcprod>
</procstep>
<procstep>
<srcused>LANDFIRE shrub cover</srcused>
<procdesc>Downloaded shrub cover data was reclassified to integers between 0 and 9, such that:<br />1 - >=10% & <20%<br />2 - >=20% & <30%<br />3 - >=30% & <40%<br />4 - >=40% & <50%<br />5 - >=50% & <60%<br />6 - >=60% & <70%<br />7 - >=70% & <80%<br />8 - >=80% & <90%<br />9 - >=90% & <=100%<br />0 - all other classes</procdesc>
<procdate>Unknown</procdate>
<srcprod>sc</srcprod>
</procstep>
<procstep>
<srcused>NLCD land cover data</srcused>
<procdesc>NLCD tree cover (tc) was used as is, with integer values between 0 and 100 representing percent tree cover.<br /><br />NLCD land cover classes (dev) were used as a mask to later exclude non-natural vegetation and non-vegetation. Notable classes include:<br />0 - Unclassified<br />11 - Open Water<br />12 - Perennial Snow/Ice<br />21 through 24 - Developed (all intensities)<br />31 - Barren Land<br />82 - Cultivated Crops<br />All class codes can be found at https://www.mrlc.gov/nlcd06_leg.php</procdesc>
<procdate>Unknown</procdate>
<srcprod>tc<br/>dev</srcprod>
</procstep>
<procstep>
<srcused>NASS land cover data</srcused>
<procdesc>NASS data (nass) was used as a mask to later exclude non-natural vegetation and non-vegetation. Notable classes include:<br />0 - Background<br />1 through 77 - Agriculture (specific crops)<br />111 - Open Water<br />112 - Perennial Ice/Snow<br />121 through 124 - Developed (all intensities)<br />131 - Barren<br />205 through 247 - Agriculture (specific crops)<br />All class codes can be found at https://www.nass.usda.gov/Research_and_Science/Cropland/metadata/2015_cultivated_layer_metadata.php</procdesc>
<procdate>Unknown</procdate>
<srcprod>nass</srcprod>
</procstep>
<procstep>
<srcused>Segmented polygons<br />ele<br />slp<br />casp<br />sasp<br />twi<br />lcv<br />sri<br />tpi<br />minpr<br />maxpr<br />aws<br />clay<br />sand<br />silt<br />cec<br />d2r<br />ph<br />om<br />caco3<br />tsf<br />ff<br />tc<br />sc<br />nass<br />dev</srcused>
<procdesc>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.<br /><br />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.<br /><br />The result was the final set of Idaho habitat patches, indexed by 1:24,000 quadrangle.</procdesc>
<procdate>Unknown</procdate>
<srcprod>Idaho habitat patches<br />id<br />quad<br />long<br />lat<br />shp_area</srcprod>
</procstep>
<procstep>
<srcused>Idaho habitat patches<br />BLM vegetation data<br />IDFG vegetation data</srcused>
<procdesc>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.<br /><br />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.<br /><br />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.<br /><br />For more details on modelling, see the cross-referenced article, McCarley et al. 2020.</procdesc>
<procdate>Unknown</procdate>
<srcprod>Plant species models</srcprod>
</procstep>
</lineage>
</dataqual>
<spdoinfo>
<direct>Vector</direct>
</spdoinfo>
<spref>
<horizsys>
<planar>
<gridsys>
<gridsysn>Universal Transverse Mercator</gridsysn>
<utm>
<transmer>
<longcm>-114.0</longcm>
<latprjo>42.0</latprjo>
<feast>2500000.0</feast>
<fnorth>1200000.0</fnorth>
<sfctrmer>0.9996</sfctrmer>
</transmer>
</utm>
</gridsys>
<planci>
<coordrep>
<absres>0.00000000222002416450096</absres>
<ordres>0.00000000222002416450096</ordres>
</coordrep>
<plance>Coordinate Pair</plance>
<plandu>Meters</plandu>
</planci>
</planar>
<geodetic>
<ellips>Geodetic Reference System 80</ellips>
<semiaxis>6378137.0000</semiaxis>
<denflat>298.257222101</denflat>
</geodetic>
</horizsys>
</spref>
<eainfo>
<detailed>
<enttyp>
<enttypl>Idaho habitat patches</enttypl>
<enttypd>Segmented polygons (habitat patches) attributed with environmental variables.</enttypd>
<enttypds>Derived from object-oriented segmentation of 2011 and 2015 National Agriculture Imagery Program (NAIP) imagery and attributed with other data sources.</enttypds>
</enttyp>
<attr>
<attrlabl>id</attrlabl>
<attrdef>Unique polygon identifier</attrdef>
</attr>
<attr>
<attrlabl>mean_blue</attrlabl>
<attrdef>Mean blue NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>mean_green</attrlabl>
<attrdef>Mean green NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>mean_red</attrlabl>
<attrdef>Mean red NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>mean_nir</attrlabl>
<attrdef>Mean near-infrared NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>sd_blue</attrlabl>
<attrdef>Standard deviation blue NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>sd_green</attrlabl>
<attrdef>Standard deviation green NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>sd_red</attrlabl>
<attrdef>Standard deviation red NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>sd_nir</attrlabl>
<attrdef>Standard deviation near-infrared NAIP reflectance</attrdef>
</attr>
<attr>
<attrlabl>quad</attrlabl>
<attrdef>USGS 1:24,000 quadrangle unique identifier, prefixed with 'q'</attrdef>
</attr>
<attr>
<attrlabl>ele</attrlabl>
<attrdef>Elevation in meters</attrdef>
</attr>
<attr>
<attrlabl>slp</attrlabl>
<attrdef>Slope in degrees</attrdef>
</attr>
<attr>
<attrlabl>casp</attrlabl>
<attrdef>Cosine aspect, describes how north (10) or south facing (-10) the slope is, with 0.001 representing flat areas</attrdef>
</attr>
<attr>
<attrlabl>sasp</attrlabl>
<attrdef>Sine aspect, describes how east (10) or west facing (-10) the slope is, with 0.001 representing flat areas</attrdef>
</attr>
<attr>
<attrlabl>twi</attrlabl>
<attrdef>Topographic wetness index, classifying hilltops, valley bottoms, exposed ridges, flat plains, and upper or lower slope</attrdef>
</attr>
<attr>
<attrlabl>lcv</attrlabl>
<attrdef>Landscape curvature index, describes upwardly convex (+ value), upwardly concave (- value), and flat (0)</attrdef>
</attr>
<attr>
<attrlabl>sri</attrlabl>
<attrdef>Solar radiation index, insolation (WH/m2) during main growing season</attrdef>
</attr>
<attr>
<attrlabl>tpi</attrlabl>
<attrdef>Topographic slope position index, describing position higher than surrounding (+ value), lower than surrounding (- value), or similar (0)</attrdef>
</attr>
<attr>
<attrlabl>minpr</attrlabl>
<attrdef>30-year average minimum precipitation (mm)</attrdef>
</attr>
<attr>
<attrlabl>maxpr</attrlabl>
<attrdef>30-year average maximum precipitation (mm)</attrdef>
</attr>
<attr>
<attrlabl>tapr</attrlabl>
<attrdef>30-year average annual precipitation (mm)</attrdef>
</attr>
<attr>
<attrlabl>mintp</attrlabl>
<attrdef>30-year average minimum temperature (deg C)</attrdef>
</attr>
<attr>
<attrlabl>maxtp</attrlabl>
<attrdef>30-year average maximum temperature (deg C)</attrdef>
</attr>
<attr>
<attrlabl>aws</attrlabl>
<attrdef>Available water supply (cm) from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>clay</attrlabl>
<attrdef>Percent clay from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>sand</attrlabl>
<attrdef>Percent sand from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>silt</attrlabl>
<attrdef>Percent silt from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>cec</attrlabl>
<attrdef>Cation exchange capacity (CEC-7; milliequivalents/100g) from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>d2r</attrlabl>
<attrdef>Depth to any restrictive layer (cm)</attrdef>
</attr>
<attr>
<attrlabl>ph</attrlabl>
<attrdef>pH from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>om</attrlabl>
<attrdef>Percent organic matter from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>caco3</attrlabl>
<attrdef>Percent calcium carbonate from surface to 25cm depth</attrdef>
</attr>
<attr>
<attrlabl>tsf</attrlabl>
<attrdef>Time since most recent fire (years), only going back to 1984 (31 years)</attrdef>
</attr>
<attr>
<attrlabl>ff</attrlabl>
<attrdef>Fire frequency (number of fires), only going back to 1984</attrdef>
</attr>
<attr>
<attrlabl>tc</attrlabl>
<attrdef>Percent tree cover (to nearest 1%)</attrdef>
</attr>
<attr>
<attrlabl>sc</attrlabl>
<attrdef>Percent shrub cover (indexed to 10% increments)</attrdef>
</attr>
<attr>
<attrlabl>nass</attrlabl>
<attrdef>USDA National Agricultural Statistics Service land cover data (2014)</attrdef>
</attr>
<attr>
<attrlabl>dev</attrlabl>
<attrdef>National Land Cover Database land cover data (2011)</attrdef>
</attr>
<attr>
<attrlabl>long</attrlabl>
<attrdef>Longitude of polygon center (in native UTM coordinates)</attrdef>
</attr>
<attr>
<attrlabl>lat</attrlabl>
<attrdef>Latitude of polygon center (in native UTM coordinates)</attrdef>
</attr>
<attr>
<attrlabl>shp_area</attrlabl>
<attrdef>Area (m2) of polygon</attrdef>
</attr>
</detailed>
<overview>
<eadetcit>none provided</eadetcit>
<eaover>Below you will find the data available in this archive and a short description of it's contents. See the cross-referenced article, McCarley et al. 2020 for more information about the lasso logistic regression models.<br /><br />DATA FILES<br /><br />\Data\IdahoPolygons\*.zip: Shapefiles of the Idaho Habitat Patches organized by USGS 1:24,000 quadrangles unique identifier "UID" prefixed with the letter "q".<br />\Data\SDMs\distal_proximal.zip: R Data Files containing the lasso logistic regression models using distal and proximal variables organize by USDA species code.<br />\Data\SDMs\proximal.zip: R Data Files containing the lasso logistic regression models using proximal variables organize by USDA species code.<br /><br />SUPPLEMENTAL FILES<br />\Supplamental\IdahoPolygons\USGS24k.zip: Shapefile of USGS 1:24,000 quadrangles in Idaho.<br />\Supplamental\SDMs\applyModel.R: R script/function for applying species distribution models to the habitat patches.</eaover>
</overview>
</eainfo>
<metainfo>
<metd>20210126</metd>
<metc>
<cntinfo>
<cntperp>
<cntper>Ryan McCarley</cntper>
<cntorg>University of Idaho</cntorg>
</cntperp>
<cntaddr>
<addrtype>mailing</addrtype>
<address>MS 1133 875 Perimeter Dr</address>
<city>Moscow</city>
<state>Idaho</state>
<postal>83844</postal>
</cntaddr>
<cntvoice>none provided</cntvoice>
<cntpos>Reseach Support Scientist</cntpos>
<cntemail>tmccarley@uidaho.edu</cntemail>
</cntinfo>
</metc>
<metstdn>FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata</metstdn>
<metstdv>FGDC-STD-001.1-1999</metstdv>
</metainfo>
</metadata>