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

Chapter
Part of the Modeling Dynamic Systems book series (MDS)

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.

References

  1. Applied Biomathematics (2003) Ramas software. Copyright 1982–2007. Setauket. http://www.ramas.com/. Accessed 2/26/2010
  2. Beier P, Noss RF (1998) Do habitat corridors provide connectivity? Conserv Biol 12(6):1241–1252CrossRefGoogle Scholar
  3. Bennett AF (1999) Linkages in the Landscape: The Role of Corridors and Connectivity in Wildlife Conservation. International Union for Conservation of Nature and Natural Resources (IUCN) Forest Conservation Programme. Conserv Forest Ecosystems, ISBN 2831702216, 9782831702216, 24(1):244Google Scholar
  4. Hanski IA, Gilpin ME (eds) (1997) Metapopulation biology. Academic, San DiegoGoogle Scholar
  5. Hargrove WW, Hoffman FM, and Efroymson RA (2005) A practical map-analysis tool for detecting potential dispersal corridors. Landsc Ecol 20(4):361–373Google Scholar
  6. Noss RF (1987) Corridors in real landscapes: a reply to Simberloff and Cox. Conserv Biol 1(2):159–164CrossRefGoogle Scholar
  7. Wilensky U (1999) NetLogo. Computer software. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston. http://ccl.northwestern.edu/netlogo/. Accessed 2/26/2010

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.USDA Forest Service Southern Research StationEastern Environmental Threat Assessment CenterAshevilleUSA
  2. 2.Construction Engineering Research LaboratoryUS Army Engineer Research and Development CenterChampaignUSA

Personalised recommendations