EcoConnectivityIntegrityDissolved

<div style='text-align:Left;font-size:12pt'><div><div><p><span>Integrity layer:</span></p><p><span>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.</span></p><p><span /></p><p><span>Connectivity layer:</span></p><p><span>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.</span></p><p><span>These two layers were combined using the Count Overlap tool. </span></p></div></div></div>

完整性图层（Integrity layer）：
本图层表征针对六个多边形源数据集的复合计数重叠度，这些数据集考量了生态系统的结构、功能与组成，旨在估算高分水岭（High Divide）区域的相对生态完整性。
本研究参照Parrish等人（2003）、Wurtzebach与Schultz（2016）的研究范式，通过整合公开可用的空间数据（spatial data）来定义并估算生态完整性，这些数据用于描述“组成、结构、功能与生态过程要素”，具体说明如下。

连通性图层（Connectivity layer）：
本研究采用Belote等人（2022）提出的中等容差情景，搭配150米移动窗口，并基于均值（0.727）对栅格数据（raster）进行重分类，所有高于该均值的区域均被视为“适宜”连通区域。随后将该图层裁剪至研究区域范围，并转换为多边形矢量格式。
Dreiss等人（2022）基于分类连通性与气候流数据集（TNC 2020）识别出气候引导廊道，针对这些区域提取了连通性与气候流的原始数据值，并将其余数值重新缩放至0至1区间。第二份气候廊道数据集（Carroll等人2018）亦采用相同方式完成重缩放。将上述两份数据集合并后，选取合并值分布处于第80百分位及以上的区域开展分析，数据集数值越高则代表气候廊道的适宜性越强。参照Dreiss等人（2022）的方法，鉴于该数据集为全国尺度，仅取前33%与前50%分位均无法在研究区域内呈现有效数据，因此本研究选取气候引导野生动物廊道的数值前66%分位区间。最终将该图层裁剪至研究区域范围，并转换为多边形矢量格式。

采用计数重叠工具（Count Overlap tool）将上述两个图层进行合并。