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    <Esri>
        <CreaDate>20230804</CreaDate>
        <CreaTime>14145300</CreaTime>
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    <dataIdInfo>
        <idCitation>
            <resTitle>Global Human Modification Index</resTitle>
        </idCitation>
        <searchKeys>
            <keyword>High Divide</keyword>
            <keyword>Global Human Modification Index</keyword>
        </searchKeys>
        <idPurp>Cumulative measure of human modification of terrestrial lands.</idPurp>
        <idAbs>&lt;div style='text-align:Left; font-size:12pt;'&gt;&lt;p&gt;&lt;span&gt;Kennedy et al. (2019) provided a cumulative measure of &lt;/span&gt;&lt;span style='font-size:12pt;'&gt;human modification of terrestrial lands based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially explicit global datasets with a median year of 2016. They quantified the degree of land modification and the amount and spatial configuration of low modified lands (i.e. natural areas relatively free from human alteration)across all ecoregions and biomes. We reclassified this layer based on the lower third (.33) of the least modified places, converted to a polygon, and used this as a third indicator of structural &lt;/span&gt;&lt;span style='font-size:12pt;'&gt;intactness.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;</idAbs>
        <idCredit>Kennedy et al. (2019) - USFWS modified and clipped to High Divide Area</idCredit>
        <resConst>
            <Consts>
                <useLimit>&lt;div style='text-align:Left; font-size:12pt;'&gt;&lt;p&gt;&lt;span&gt;See Kennedy et. al. (2019)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;&lt;br /&gt;&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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</Data>
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