Finding and Filling the "Cracks" In Resistance Surfaces for Least-cost Modeling

• Main Author: Kristina Rothley
• Format: Article
• Language: English
• Published: Resilience Alliance 2005-06-01
• Series: Ecology and Society
• Subjects: connectivity analysis; cracks; least-cost modeling; resistance layers.
• Online Access: http://www.ecologyandsociety.org/vol10/iss1/art4/

Least-cost modeling is an increasingly popular method used to measure the effective distance between habitat patches and to assess the connectivity of existing landscapes or potential reserves. For least-cost models to be reliable and credible, however, the validity of input data must be demonstrated. Least-cost modelers must also exercise extreme caution when using any GIS-based analysis of this kind. Technical issues associated with the raster-based representation of spatial data may introduce errors in otherwise correct data that nullify results. In this paper, I address the potential presence of "cracks" in the resistance input layer of least-cost modeling exercises. Cracks result when narrow, costly features, such as roads or train tracks, are represented in raster form. These cracks lead to the erroneous identification of nonexistent "shortcuts" across truly expensive barriers. In this paper, I use a hypothetical example to explain resistance-layer cracks, how they occur, and the errors they generate with respect to least-cost modeling. I then present a simple algorithm to reveal the location of cracks and one approach to filling the cracks. Finally, these methods are demonstrated on a real data set in which more than 1% of the raster cells are shown to be cracks. The negative repercussions of faulty resistance layers when conducting least-cost modeling can not be overstated. On the one hand, unimportant or nonexistent least-cost paths on a landscape may be mislabeled as important. As a result, conservation resources committed to protecting these paths will be wasted. Conversely, truly critical corridors may be overlooked, jeopardizing the organisms that rely on them.