<?xml version="1.0" encoding="UTF-8" standalone="no"?><metadata xml:lang="en">
    <Esri>
        <CreaDate>20240328</CreaDate>
        <CreaTime>10155600</CreaTime>
        <ArcGISFormat>1.0</ArcGISFormat>
        <SyncOnce>TRUE</SyncOnce>
    </Esri>
    <dataIdInfo>
        <idCitation>
            <resTitle>Ecological Integrity Rank</resTitle>
        </idCitation>
        <searchKeys>
            <keyword>Integrity</keyword>
            <keyword>High Divide</keyword>
            <keyword>Ecology</keyword>
        </searchKeys>
        <idPurp>Ecological Integrity Rank</idPurp>
        <idAbs>&lt;DIV STYLE="text-align:Left;font-size:12pt"&gt;&lt;P&gt;&lt;SPAN&gt;This layer represents the composite count overlap of six polygon source data sets that consider ecosystem structure, function, and composition in order to estimate relative ecological integrity across the High Divide region. We define and estimate ecological integrity by assembling publicly available spatial data that describe “elements of composition, structure, function, and ecological processes” (after Parrish et al. 2003; Wurtzebach and Schultz 2016) as described below.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;</idAbs>
        <idCredit>Heart of the Rockies, USFWS</idCredit>
        <resConst>
            <Consts>
                <useLimit>&lt;DIV STYLE="text-align:Left;font-size:12pt"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;United States Fish and Wildlife Service (Service) shall not be held liable for improper or incorrect use of the data described and/or contained herein. While the Service makes every reasonable effort to ensure the accuracy and completeness of data provided for distribution, it may not have the necessary accuracy or completeness required for every possible intended use. The Service recommends that data users consult the associated metadata record to understand the quality and possible limitations of the data. The Service creates metadata records in accordance with the standards endorsed by the Federal Geographic Data Committee. As a result of the above considerations, the Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of the data. It is the responsibility of the data user to use the data in a manner consistent with the limitations of geospatial data in general and these data in particular. Although these data have been processed successfully on a computer system at the Service, no warranty, expressed or implied, is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This applies to the use of the data both alone and in aggregate with other data and information.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</useLimit>
            </Consts>
        </resConst>
    </dataIdInfo>
    <Binary>
        <Thumbnail>
            <Data EsriPropertyType="PictureX">/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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</Data>
        </Thumbnail>
    </Binary>
</metadata>