<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
    <Esri>
        <CreaDate>20230804</CreaDate>
        <CreaTime>15593700</CreaTime>
        <ArcGISFormat>1.0</ArcGISFormat>
        <SyncOnce>TRUE</SyncOnce>
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    <dataIdInfo>
        <idCitation>
            <resTitle>ABF Zonation Biodiversity</resTitle>
        </idCitation>
        <searchKeys>
            
            
        <keyword>Biodiversity</keyword><keyword>High Divide</keyword><keyword>species ranges</keyword></searchKeys>
        <idPurp>For an additional biodiversity layer, we used an additive benefit
function zonation (Belote et al. 2021). This method took 1697 suitable species ranges (CONUS GAP 2018), each coarsened to ~5km resolution, calculated biodiversity prioritization based on beta diversity</idPurp>
        <idAbs>&lt;div style='text-align:Left; font-size:12pt;'&gt;&lt;p&gt;&lt;span&gt;For an additional biodiversity layer, we used an additive benefit &lt;/span&gt;&lt;span style='font-size:12pt;'&gt;function zonation (Belote et al. 2021). This method took 1697 suitable species ranges (CONUS GAP 2018), each coarsened to ~5km resolution, calculated biodiversity prioritization based on beta diversity. Then using the ABF method, which is a zonation complementarity-based model &lt;/span&gt;&lt;span style='font-size:12pt;'&gt;that iteratively removes grid cells based on core-area zonation (more heavily rates species richness), it assigned priorities to grid cell locations that best represent species composition (maximizing gamma diversity by prioritizing sites with high complementarity). We reclassified the raster with the top third of values (greater than .794957) = 1, other values = 0, converted to apolygon layer, and extracted values of 1.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;</idAbs>
        <idCredit>Travis Belote (Belote, et.al, 2021) modified by Heart of Rockies/USFWS</idCredit>
        <resConst>
            <Consts>
                <useLimit>&lt;div style='text-align:Left; font-size:12pt;'&gt;&lt;p&gt;&lt;span&gt;See use limitation in Belote et. al. 2021&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style='font-family:&amp;quot;Avenir Next W01&amp;quot;, &amp;quot;Avenir Next W00&amp;quot;, &amp;quot;Avenir Next&amp;quot;, Avenir, &amp;quot;Helvetica Neue&amp;quot;, sans-serif;'&gt;Although these data and information have been processed successfully on a computer system at the U.S. Fish and Wildlife Service, USFWS, no warranty expressed or implied is made regarding the accuracy or utility of the data and information on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Inherent in any data set used to develop graphical representations are limitations of accuracy as determined by, among others, the source, scale and resolution of the data. This disclaimer applies both to individual use of the data and information, and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with this data and information, and aggregate use with other data and information. The USFWS is not liable for the user’s improper or incorrect use of the data and information described and/or contained herein. These data and any derived products are not legal documents and are not intended to be used as such. The information contained in these data may be dynamic and could change over time. The data are not better than the original sources from which they are derived. It is the responsibility of the data user to use the data appropriately and consistent with the limitations of geospatial data in general and these data in particular. It is strongly recommended that the data described or contained herein be acquired directly from an authorized USFWS source and not indirectly through some other sources which may have changed the data in some way. The USFWS is not liable for data or information that indirectly acquired through other sources.&lt;/span&gt;&lt;span&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;</useLimit>
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        </resConst>
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</Data>
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