Optimal Wildlife Corridor Placement: Washington State

Fig. 1: These maps show the criteria for what makes a landscape easy or hard to traverse for wildlife. Landcover is the most important factor, and was given twice the weight of other variables in the model used in this analysis. Distance from highways, human population density,  and “ruggedness” are all given equal importance. The closer an area to a highway, the worse it is for animals. The closer an area to a densely populated area, the worse it is for animals. The more rugged a landscape, the better it is for animals. Ruggedness refers to how much elevation changes, meaning how steep a region is.   

Fig. 2: This collection of maps shows the reclassified variables from the previous map as well as a “Wildness Index” which combines all four variables into one dataset. Each dataset was reclassified based on a scale of one to five. One representing the easiest terrain to traverse, and five representing the hardest terrain to traverse. Then, a weighted average was taken, (where land cover was given twice the weight of other datasets), generating the Wildness Index. The Wildness Index shows how easy or hard any grid cell is to migrate across for animals. 

Fig. 3: Shows the best place to put wildlife corridors based on the wildness index and distance from highly protected regions. The “distance from protected regions” dataset was weighted with the cost surface (wildness index) to produce a weighted distance dataset, where green represents close proximity and white represents extreme distance. Unlike the original distance dataset, this takes into account the difficulty, or cost, of traversing the areas given by the wildness index. The red lines, labeled “suggested corridor paths,” are the lines of least distance connecting the protected regions. While they are not the shortest straight-line paths, they are likely the easiest for wildlife to migrate across. 

Fig. 4: Model used to generate the suggested corridor paths. The model uses a digital elevation model, highways dataset, census blocks dataset, protected region dataset, and landcover dataset as inputs (seen in blue) First, the variables are reclassified based on a qualitative 1-5 scale of how easy it is for animals to migrate given those variables. For example, it is harder for animals to migrate in an area with high population density, so those regions are given a 5. Then, these variables are combined with a “weighted overlay,” which takes the weighted average and produces the Wildness Index. Finally, the “optimal region connections tool” uses the Wildness Index as a cost surface to generate the optimal corridor paths.