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        <CreaDate>20230804</CreaDate>
        <CreaTime>16234000</CreaTime>
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        <idCitation>
            <resTitle>Ecoregion Scale Climate Refugia</resTitle>
        </idCitation>
        <searchKeys>
            <keyword>Ecoregion</keyword>
            <keyword>High Divide</keyword>
            <keyword>Climate Refugia</keyword>
        </searchKeys>
        <idPurp>Reclassification of a compilation of 8 refugia datasets.</idPurp>
        <idAbs>&lt;div style='text-align:Left; font-size:12pt;'&gt;&lt;p&gt;&lt;span&gt;Dreiss et al. (2022) combined eight refugia datasets (current &lt;/span&gt;&lt;span style='font-size:12pt;'&gt;climate diversity, ecotypic diversity, land facet diversity, landscape diversity, bird macrorefugia, climate dissimilarity, climate velocity, and tree macrorefugia) using a weighted principal components analysis. We took the ecoregion scale composite layer, reclassified to a 0/1 binary using the top third of values (6.7694) and converted 1’s to polygons. Because this layer already represents several facets of ecosystem diversity, we chose to use it as the sole indicator of ecosystem function for our first draft intactness model.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;See limitations in Dreiss et al. (2022)&lt;span style='font-size:12pt;'&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;</idAbs>
        <idCredit>Dreiss, L. M., Lacey, L. M., Weber, T. C., Delach, A., Niederman, T. E., &amp; Malcom, J. W. (2022). Targeting current species ranges and carbon stocks fails to conserve biodiversity in a changing climate: Opportunities to support climate adaptation under 30 × 30. Environmental Research Letters, 17(2), 024033. https://doi.org/10.1088/1748-9326/ac4f8c modified by Heart of Rockies and USFWS</idCredit>
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                <useLimit>&lt;div style='text-align:Left; font-size:12pt;'&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; font-size:12pt;'&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;br /&gt;&lt;/p&gt;&lt;/div&gt;</useLimit>
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</Data>
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