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    <Esri>
        <CreaDate>20230810</CreaDate>
        <CreaTime>13152200</CreaTime>
        <ArcGISFormat>1.0</ArcGISFormat>
        <SyncOnce>TRUE</SyncOnce>
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    <dataIdInfo>
        <idCitation>
            <resTitle>Ecological Connectivity and Integrity Updated</resTitle>
        </idCitation>
        <searchKeys>
            <keyword>Connectivity</keyword>
            <keyword>Integrity</keyword>
            <keyword>High Divide</keyword>
        </searchKeys>
        <idPurp>This layers is a union of the Ecological Connectivity and Integrity layers.</idPurp>
        <idAbs>&lt;div style='text-align:Left;font-size:12pt'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;Integrity layer:&lt;/span&gt;&lt;/p&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;p&gt;&lt;span /&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Connectivity layer:&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;From Belote et al. 2022, we used the middle tolerance scenario with a 150 m moving window and reclassified raster based on the mean value (.727). Everything above the mean was considered "suitable" connectivity. The layer was clipped to the analysis area and converted into a polygon.  Dreiss et al. (2022) extracted raw data values on connectivity and climate flow for areas that were IDed as climate-informed corridors based on categorical connectivity and climate flow dataset (TNC 2020). The remaining values were rescaled to fall between 0 and 1. A second climate corridor dataset (Carroll et al. 2018) was similarly rescaled. These two datasets were combined and locations in the 80th percentile of the distribution of combined values were analyzed. Higher values in the dataset indicate more optimal climate corridors. From Dreiss et al. 2022, here we took the upper 66% of values from the climate-informed wildlife corridors, as the top 33% and 50% were both insufficient to show data in the region given the dataset's national scale. The layer was clipped to the analysis area and converted into a polygon.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;These two layers were combined using the Count Overlap tool. &lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
        <idCredit>Heart of the Rockies, USFWS, Modeified from Dreiss et al, Belote et al and other sources (see metadata for individual source layers).</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;'&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;br /&gt;&lt;/p&gt;&lt;/div&gt;</useLimit>
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</Data>
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