Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis

By Stephen F. Spear, Niko Balkenhol, Marie-Josée Fortin, B. Mcrae, K. Scribner. Molecular Ecology, 2010

654 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/99f0495697ffeb21725c274c6f0c56dcc2c02e5d

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Citations in the corpus

ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms (2014, background): “As defined by Spear, Balkenhol, Fortin, McRae, and Scribner (2010), a resistance surface is a spatial layer that assigns a value to each landscape or environmental feature, with values representing the extent to which that feature impedes or facilitates connectivity for an organism.”
Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods (2014, background): “Most assessments of population connectivity are based on applying connectivity models, synoptic or otherwise, to resistance surfaces [5–7,13].”
Multispecies assessment of core areas and connectivity of desert carnivores in central Iran (2018, background): “…models should be parameterized based on empirical movement data of dispersing individuals (e.g., Elliot, Cushman, Macdonald, & Loveridge, 2014; Spear et al., 2010) or genetic data (Braunisch, Segelbacher, & Hirzel, 2010; Castillo, Epps, Davis, & Cushman, 2014; Cushman, McKelvey, Hayden, &…”
A conceptual framework for the spatial analysis of landscape genetic data (2013, background): “…on characteristics of the natal or new site as well as the physical distance between sites (IBD) and the nature of the intervening landscape (matrix resistance), including the presence of complete barriers to movement or more gradual differences in traversability or mortality (Spear et al. 2010).”
Evaluating population connectivity for species of conservation concern in the American Great Plains (2013, positive): “Modeled predictions of core habitat areas, fracture zones, and corridors have sometimes been criticized because they lack supporting movement data (Simberloff et al. 1992; Rosenberg et al. 1997) and because they may contain errors in model parameters or incorrect assumptions (Spear et al. 2010).”
Dispersal strength determines meta‐community structure in a dendritic riverine network (2015, background): “on expert opinion (a practice that some have questioned; Spear et al., 2010).”
Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern (2012, methodology): “1994), and landscape-cost distance model (i.e. IBR distance; e.g. Cushman et al. 2006; Spear et al. 2010) was calculated for each pair of sampling locations as the cumulative cost associated with traversing the least cost path from one sampling location to the other using COSTDISTANCE in ARCGIS vers”
Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data (2015, methodology): “…issue related to genetic data is that in long-lived species there may be a temporal disconnection between genetic structure and the current landscape; i.e., there may be legacy effects of previous landscapes (James et al. 2007, Spear et al. 2010) that could lead to misestimate current connectivity.”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “Due to the complexity of this task, many options exist for parameterizing and evaluating resistance surfaces, and we refer readers to the comprehensive reviews of Spear et al. (2010, 2016) for further details.”
Linking ecosystem services and circuit theory to identify ecological security patterns. (2018, background): “The resistance surface characterizes the influence of landscape heterogeneity on the flow of ecological process (Beier et al., 2008; Spear et al., 2010; Adriaensen et al., 2003).”
Ecological connectivity research in urban areas (2015, background): “For example, more studies should combine landscape resistance modelling (Spear et al. 2010; Zeller, McGarigal & Whiteley 2012) with landscape genetics to validate hypothesized influences of urban features on functional connectivity (e.g. MunshiSouth 2012).”
SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses (2014, background): “However, few studies have access to meaningful friction landscapes (Spear et al. 2010).”
Estimating landscape resistance to movement: a review (2012, background): “Moreover, no general consensus has been reached regarding the most accurate data sources and analytical methods for modeling resistance surfaces (Spear et al. 2010).”
Circuit Theory and Model-Based Inference for Landscape Connectivity (2013, positive): “This is a critical point, as the results of spatial connectivity studies are being used to influence policy decisions (e.g., Theobald, Crooks, and Norman 2011) and predict the results of landscape change over time (e.g., Spear et al. 2010).”