The effect of cost surface parameterization on landscape resistance estimates

Abstract

A cost or resistance surface is a representation of a landscape's permeability to animal movement or gene flow and is a tool for measuring functional connectivity in landscape ecology and genetics studies. Parameterizing cost surfaces by assigning weights to different landscape elements has been challenging however, because true costs are rarely known; thus, expert opinion is often used to derive relative weights. Assigning weights would be made easier if the sensitivity of different landscape resistance estimates to relative costs was known. We carried out a sensitivity analysis of three methods to parameterize a cost surface and two models of landscape permeability: least cost path and effective resistance. We found two qualitatively different responses to varying cost weights: linear and asymptotic changes. The most sensitive models (i.e. those leading to linear change) were accumulated least cost and effective resistance estimates on a surface coded as resistance (i.e. where high-quality elements were held constant at a low-value, and low-quality elements were varied at higher values). All other cost surface scenarios led to asymptotic change. Developing a cost surface that produces a linear response of landscape resistance estimates to cost weight variation will improve the accuracy of functional connectivity estimates, especially when cost weights are selected through statistical model fitting procedures. On the other hand, for studies where cost weights are unknown and model selection is not being used, methods where resistance estimates vary asymptotically with cost weights may be more appropriate, because of their relative insensitivity to parameterization.

Landscape connectivity for wildlife: development and validation of multispecies linkage maps

“…We randomly labelled 10% of the 10 000 pixels as good habitat by weighting them with a cost of 10 and weighting the remaining pixels as poor habitat with a cost of 10 000 (i.e. a resistance surface; Koen, Bowman & Walpole 2012). We used circuit theory (McRae & Beier 2007;McRae et al 2008) and Circuitscape software (version 3.5.8, McRae & Shah 2009) to highlight areas with a relatively high probability of use as movement corridors (i.e.…”

Beyond species distribution modeling: A landscape genetics approach to investigating range shifts under future climate change

“…Like other landscape genetics studies, the novel approach described in this study may be sensitive to the parameterisation of the landscape resistance or cost surfaces that are used as a measure of functional connectivity (Koen et al 2012). To avoid biases, this study primarily used continuous variables that can be transformed into linear cumulative resistance values bound between 1 and 100, and retained a similar cumulative linear approach with the categorical A C C E P T E D M A N U S C R I P T .…”

Modelling Marten (Martes americana) Movement Costs in a Boreal Forest: Effects of Grain Size and Thematic Resolution

“…We arbitrarily ascribed the highest resistance to movement in open areas, with ten values ranging from 4 to 1000 (constant set of values in mature forest, see [28]). Minimal resistance to open areas was set to 4 in order to vary young forest resistance value relative to the resistance in open habitat with ratios of 14, 12 and 34.…”

“…Resistances >1000 often generated simulated movements along the border of the study area and were assumed to be out of a biologically realistic range for marten. Due to habitat selection by American marten [29], we assumed that mature forest stands offered the lowest resistance to marten movements, and set its value to 1 in all resistance maps [28].…”

“…A third scenario partitioned the landscape into: "Open habitat," "Young forest" (11 to 30 y-old), and "Mature" (>30 y-old), with no edge effect. The latter scenario was subdivided in three subgroups, according to the resistance of young stands relative to that of open areas (ratio = 1/4, 1/2, or 3/4; Table 1; relative cost weights [28]). Each scenario was represented by its own series of 10 maps differing by resistance in open areas (see above).…”