These layer packages contain the results of the Linkage Mapper tools as described below.
Running Five of the Linkage Mapper ToolsThe Linkage Mapper software package includes six tools. For this study we used five of these tools, producing stand-alone products which we also combined to create synthesized products. We used Linkage Mapper v2.1Beta5 and an ArcGIS modelbuilder script to run Linkage Pathways, Pinchpoint Mapper, Centrality Mapper, the Linkage Priority tool, and Barrier Mapper using the above described resistance surfaces and habitat concentration areas (HCAs) layers. We used the default parameters described in the respective user guides except where otherwise noted below.
The following are the high level methods and some screengrabs of the results. Interactive maps are provided in the Data Basin Gallery (https://databasin.org/galleries/f08cebd507f2445a9ca94314fb58fd9a), and the data are also available on Science Base.
The Linkage Pathways Tool (originally called Linkage Mapper before the rest of the tools in the toolbox were developed) maps the linkages and quantifies the value of each path within a linkage (B. H. McRae and Kavanagh 2011). This results in “least cost corridors”. We used an informal sensitivity analysis (visually evaluating mapped results using various parameter values) to establish a maximum linkage width of 800,000 cost-weighted distance units. We chose this relatively large width with the intention of using a color ramp with many colors, thereby being able to show visually where the high quality linkage locations are (corresponding to about 100,000 units in the “electric green” color in the map below) as well as the other areas that could be pathways between HCAs. This tool yielded the Linkage Pathways output (i.e. least-cost corridors), which is the standard connectivity output. It also yielded an auxiliary output that is useful for visualizing where individuals might feasibly explore for dispersal events: the cost-weighted distance layer (provided on Science Base and Data Basin).
We used Pinchpoint Mapper, which uses circuit theory by calling upon Circuitscape software (McRae et al. 2008), to help quantify the conservation priority of portions of a linkage that are dangerously narrow (B. H. McRae 2012c). We used the “all pairs” evaluation rather than just adjacent pairs, and transformed the result to emphasize not only the pinchpoints region-wide, but to also shed light on the pinchpoints within each linkage. We did this by slicing the result into ten classes, and used the Jenks natural breaks function within ArcGIS, that achieves the normalization objective.
We used Centrality Mapper, to calculate “current flow centrality” across the networks (B. H. McRae 2012b). Current flow centrality is a measure of how important a linkage is for keeping the overall network connected. This tool uses Circuit Theory and graph theory by calling upon Circuitscape algorithm from within the Linkage Mapper software.
Then, we used the Linkage Priority Tool that quantifies which linkages and core areas are most valuable (Gallo and Greene 2018). Core area priority value is a function of shape, mean resistance value, climate refugia, and expert opinion values, if available. Linkage priority value is a function of the priority value of the cores being connected, and of the characteristics of each linkage, such as permeability (i.e., the mean resistance values along the least cost path), the length, the centrality, and expert opinion if available. For this study, we used several different combinations of these variables, including a run in which climate considerations were included (available upon request), and the one that the working group felt to be the most parsimonious with the following criteria and weights: mean permeability (0.43), proximity (shorter linkages are better than longer, all else being equal)(0.17) and the average centrality value of the two cores being connected (0.4). These criteria and values were derived by an online poll with the WWHCWG. The values were the mean value of all the suggested weight values for a criterion, among all participants in the poll.
Finally, we had the multi-tool modelbuilder script call the Barrier Mapper tool that quantifies the importance of “barriers” that affect the quality and/or location of the corridors (B. H. McRae 2012a). The term “barriers” is used in a general way and includes portions of a landscape that are difficult to pass through for wildlife, but are not barriers in the formal sense of the term. In essence, they identify areas for restoration and mitigation. Much more detail is provided in a publication by McRae et al. (2012). We examined the results of five different detection radii values, and chose 180 m because it best supported an average restoration project’s size and scope.
Due to project scope and budget, we decided against running the sixth Linkage Mapper tool: Climate Linkage Mapper. This tool would have shown how the spatial configurations of each linkage are expected to change with climate change. The other aspect of Climate Linkage mapper: prioritizing linkages based on climate gradients, is now also addressed in Linkage Priority tool.
Synthesis of Connectivity Priority Metrics (i.e. of tool outputs)
Similar to what we have done in six other regions, including three in California (Gallo et al. 2019), and the Klamath Basin Region (Spencer et al. 2019), we combined the above intermediate datasets into a single synthesis layer, called Combined Connectivity Value, using evenly weighted sums, as shown in the figure below.
Recognizing that some cores may be more important to population resilience than others and that movement through core areas may also be important (especially given the narrow, convoluted configuration and dynamic habitat values of fisher cores in the Klamath region), we included core area value in the “Linkage Mapper Connectivity with Cores” layer (also known as Estimated Conservation Priority Areas)
Next we selected the highest restoration priority values from the barrier mapper output, scaled it linearly from 0-1 (with all other areas as null values), combined it in a weighted sum with the Combined Conectivity Value layer, and rescaled the output linearly to range from 0-1.
