Areas of both high connectivity value and residential development risk developed for the Washington Habitat Connectivity Action Plan.

To identify areas with concentrated extents of high connectivity value which are also facing significant threat from potential housing and residential development, we applied kernel density analyses following the methodology detailed in the preceding section by adjusting the cost surface to account for the development.

To specifically quantify areas under high development pressure, we employed the Mann-Kendall Index. This non-parametric test was computed to detect statistically significant monotonic trends in a 30-year Landsat satellite image time series. For each landscape unit, the Mann-Kendall Index yielded a value where positive indices indicated an increasing trend in development pressure. We rescaled the positive index values to a range of 0 to 1, with 1 representing the highest development pressure.

This rescaled development pressure layer was subsequently integrated with areas of relatively high connectivity. To achieve this, we selected portions of the original connectivity surface with values exceeding the 30th percentile (connectivity value > 8) and rescaling it to a 0-to-1 range, where 1 denoted the highest connectivity within this subset.

Finally, we multiplied the rescaled development pressure layer by the rescaled high-connectivity layer. The resulting surface, ranging from 0 to 1, represented the spatial congruence of high development threat and high connectivity, signifying areas of heightened vulnerability.

We used the multiplication layer as the base to generate source locations and ’cost surface’ to fit and compute kernel density. We set the kernel cost-weighted distance at 79,200 – an equivalent of 15 mi in uniform landscape with the lowest cost defined by highest development and connectivity values

Areas of both high connectivity value and residential development risk developed for the Washington Habitat Connectivity Action Plan.

To identify areas with concentrated extents of high connectivity value which are also facing significant threat from potential housing and residential development, we applied kernel density analyses following the methodology detailed in the preceding section by adjusting the cost surface to account for the development.

To specifically quantify areas under high development pressure, we employed the Mann-Kendall Index. This non-parametric test was computed to detect statistically significant monotonic trends in a 30-year Landsat satellite image time series. For each landscape unit, the Mann-Kendall Index yielded a value where positive indices indicated an increasing trend in development pressure. We rescaled the positive index values to a range of 0 to 1, with 1 representing the highest development pressure.

This rescaled development pressure layer was subsequently integrated with areas of relatively high connectivity. To achieve this, we selected portions of the original connectivity surface with values exceeding the 30th percentile (connectivity value > 8) and rescaling it to a 0-to-1 range, where 1 denoted the highest connectivity within this subset.

Finally, we multiplied the rescaled development pressure layer by the rescaled high-connectivity layer. The resulting surface, ranging from 0 to 1, represented the spatial congruence of high development threat and high connectivity, signifying areas of heightened vulnerability.

We used the multiplication layer as the base to generate source locations and ’cost surface’ to fit and compute kernel density. We set the kernel cost-weighted distance at 79,200 – an equivalent of 15 mi in uniform landscape with the lowest cost defined by highest development and connectivity values