An Implementation of the Pathway Analysis Through Habitat (PATH) Algorithm Using NetLogo

When developing plans to protect populations of species at risk, we tend to focus on protecting and preserving habitat where the species naturally occurs. Ecologists employ historic sightings of individuals, habitat suitability index (HSI) models, and the expert advice of local naturalists familiar with the environments desired and required. In many cases, the optimal habitats are fragmented to the extent that a species at risk could be extirpated from any single area by storms, drought, disease, or other local insult. Alternately, over time, a small population could become so inbred that it becomes susceptible to disturbances that increase the probability of local extirpation. However, if nearby populations can reach the isolated population across the fragmented space, then the isolated population becomes part of a larger metapopulation. The strength of habitat connections, therefore, can be critical to ensure the viability of at-risk populations over time. Habitat connectivity plays this important role by increasing the effective population size, maintaining gene fl ow, and facilitating regular migration, dispersal, and recolonization. Each of these processes helps ensure the long-term persistence of a population. A connected landscape is preferable to a fragmented one (Beier and Noss 1998 ; Bennett 1999 ). Natural landscapes are generally more connected than landscapes altered or manipulated by humans, so establishing or maintaining corridors is a viable strategy to sustain the natural connectivity (Noss 1987 ) .