This map of Local connectedness measures how impaired the structural connections are between natural ecosystems within a local landscape. Roads, development, noise, exposed areas, dams, and other structures all directly alter processes and create resistance to species movement by increasing the risk (or perceived risk) of harm. This dataset is an important component of resilience because it indicates whether a process is likely to be disrupted or how much access a species has to the microclimates within its given neighborhood.

The method used to map local connectedness for the region was resistant kernel analysis, developed and run by Brad Compton using software developed by the UMASS CAPS program (Compton et al. 2007, http://www.umasscaps.org). Connectedness refers to the connectivity of a focal cell to its ecological neighborhood when it is viewed as a source; in other words, it asks the question: to what extent are ecological flows outward from that cell impeded or facilitated by the surroundinglandscape? Specifically, each cell is coded with a resistance value base on land cover and roads, which are in turn assigned resistance weights by the user. The theoretical spread of a species or process outward from a focal cell is a function of the resistance values of the neighboring cells and their distance from the focal cell out to a maximum distance of three kilometers.

To calculate this metric, resistance weights were assigned to the elements of a land cover/road map. A variety of methods have been developed for determining resistance weights, in particular metrics of ecological similarity in community types (e.g. oak forest to oak forest assumed to be more connected than oak forest to spruce forest) have been used to good effect (B. Compton personal communication 2009, Compton et al. 2007). However, our weighting scheme was intentionally more generalized, such that any natural cover adjacent to other natural cover was scored as highly connected. We did not differentiate between forest types, and only slightly between open wetland and upland habitats (Table 1). Our assumption was that the requirements for movement and flows through natural landscape were less specific than the requirements for breeding, and that physical landscapes are naturally composed of an interacting mosaic of different ecosystems. Our goal was to locate areas where these arrays occur in such a way as o maintain their natural relationships and the connections between all types of flows, both material processes and species movements, not to maximize permeability for a single species (Hunter and Sulzer 2002, Ferrari and Ferrarini 2008, Forman and Godron 1986).

The resistance grid i was based on a 90-meter classified land use map with roads embedded in the grid. The source data was the 2001 NLCD for United States and NALC 2005 for Canada that identify each grid cell as one of 16 classes of land cover (NALCMS 2005). We used 90-meter grid cells to make a reasonable processing time because the CAPS software program is computationally intense.

The final result was a grid of 90-meter cells for the entire region where each cell was scored with a local connectivity value from 0 (least connected) to 100 (most connected).