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

Abstract

Habitat connectivity plays a central role in wildlife population viability by increasing the available population size, maintaining gene flow among diverse metapopulations, and facilitating regular migration, dispersal, and recolonization. This chapter documents an agent-based simulation model that can improve our understanding of species migration routes between habitat patches. It is based on the Pathway Analysis Through Habitat (PATH) algorithm, first developed for use on a supercomputer by Hargrove, Hoffman, and Efroymson (2004). Using NetLogo (http://ccl.northwestern.edu/netlogo/), the authors of this chapter created a simplified implementation of PATH that operates on a standard desktop computer. PATH identifies and highlights areas in a landscape that contribute to the natural connections among populations; identifies the metapopulation structure; and indicates the relative strength of connections holding a metapopulation together. A major benefit of this NetLogo implementation of PATH is that it does not require a supercomputer to operate. The model encapsulates essential species migration activities and costs into the bare fundamentals—a binary habitat indicator, a movement parameter, a randomness parameter, an energy-accounting function, and a mortality probability. Simulation results can provide valuable insights to support decisions that promote habitat connectivity for purposes of improved wildlife management.