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The relationship between least-cost and resistance distance

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  • Robby R Marrotte
  • Jeff Bowman

Abstract

Least-cost modelling and circuit theory are common analogs used in ecology and evolution to model gene flow or animal movement across landscapes. Least-cost modelling estimates the least-cost distance, whereas circuit theory estimates resistance distance. The bias added in choosing one method over the other has not been well documented. We designed an experiment to test whether both methods were linearly related. We also tested the sensitivity of these metrics to variation in Euclidean distance, spatial autocorrelation, the number of pixels representing the landscape, and data aggregation. We found that least-cost and resistance distance were not linearly related unless a transformation was applied. Resistance distance was less sensitive to the number of pixels representing a landscape and was also less sensitive than least-cost distance to the Euclidean distance between nodes. Spatial autocorrelation did not affect either method or the relationship between methods. Resistance distance was more sensitive to aggregation in any form compared to least-cost distance. Therefore, the metric used to infer movement or gene flow and the manipulations applied to the data used to calculate these metrics may govern findings.

Suggested Citation

  • Robby R Marrotte & Jeff Bowman, 2017. "The relationship between least-cost and resistance distance," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-19, March.
    • Handle: RePEc:plo:pone00:0174212
    DOI: 10.1371/journal.pone.0174212
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    References listed on IDEAS

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    1. Erin L Koen & Colin J Garroway & Paul J Wilson & Jeff Bowman, 2010. "The Effect of Map Boundary on Estimates of Landscape Resistance to Animal Movement," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-8, July.
    2. van Etten, Jacob, 2017. "R Package gdistance: Distances and Routes on Geographical Grids," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i13).
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    Cited by:

    1. Koenig, Shantel J. & Bender, Darren J., 2018. "Increasing the function in distance-based functional connectivity assessments: a modified spatial interaction model (SIM) approach," Ecological Modelling, Elsevier, vol. 386(C), pages 47-58.
    2. Gutiérrez, Caracé & Gancio, Juan & Cabeza, Cecilia & Rubido, Nicolás, 2021. "Finding the resistance distance and eigenvector centrality from the network’s eigenvalues," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    3. Manuel Wolff & Dagmar Haase & Jörg Priess & Tobias Leander Hoffmann, 2023. "The Role of Brownfields and Their Revitalisation for the Functional Connectivity of the Urban Tree System in a Regrowing City," Land, MDPI, vol. 12(2), pages 1-22, January.

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