Gene Flow in Complex Landscapes: Testing Multiple Hypotheses with Causal Modeling

By Samuel A. Cushman, Kevin S. McKelvey, J. Hayden, M. D. Schwartz. American Naturalist, 2006

712 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9cc5fe19fb90486c8adbb995613669521d1b3b25

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Citations in the corpus

Should the Mantel test be used in spatial analysis? (2015, methodology): “Methods in Ecology and Evolution © 2015 British Ecological Society multivariate data (Sokal 1979; Legendre & Troussellier 1988; Legendre & Fortin 1989; Cushman et al. 2006).”
Why sampling scheme matters: the effect of sampling scheme on landscape genetic results (2009, background): “Although other landscape genetic approaches exist (e. g. Double et al. 2005; Cushman et al. 2006; Storfer et al. 2007), the most common way to conduct individual-based landscape genetic analyses is to sample individuals diffusely across a landscape, detect discontinuities among mulitlocus genotypes…”
Applications of landscape genetics in conservation biology: concepts and challenges (2010, methodology): “On the other hand, using a multi-hypothesis, causal modeling approach Cushman et al. (2006) showed that gradients of landscape structure were the predominant drivers of gene flow in a study population of black bears and neither a classic Wright-Fisher discretely bounded, panmictic population…”
Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods (2014, methodology): “Consistent with our first hypothesis, we found that the resistance map produced from the movement data of black bears in this study area [15] greatly outperformed the resistance map produced from the analysis of genetic differentiation [19] across all methods of connectivity modeling (Table 1).”
Estimating landscape resistance to movement: a review (2012, methodology): “(Cushman et al. 2006) or between populations (Emaresi et al.”
Identifying future research needs in landscape genetics: where to from here? (2009, background): “Thus, if friction grids are used to test ecological hypotheses about landscape connectivity, they have to be well justified (e.g., based on independent empirical data sources or expert knowledge), or should utilize multiple cost or resistance values (Coulon et al. 2004; Cushman et al. 2006; Epps e”
ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms (2014, background): “In the absence of direct observation of movement or dispersal across the landscape, effective distances are often interpreted as functional connectivity (e.g. Cushman, McKelvey, Hayden, & Schwartz, 2006).”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, background): “Similarly, spectral data can help to model resistance surfaces which are used to estimate the effective resistance of the landscape to gene flow (Spear et al., 2005; Cushman et al., 2006; McRae and Beier, 2007).”
Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho (2010, methodology): “We utilize two extensions of the causal modeling approach in landscape genetics (Cushman et al. 2006).”
Dismantling the Mantel tests (2011, methodology): “Their study includes 300 Monte Carlo simulations, but they are all drawn from a single landscape resistance surface (identified by Cushman et al. 2006 as the most supported out of 110 alternative models for black bearUrsus americanus gene flow in northern Idaho, USA).”
Modelling long‐distance seed dispersal in heterogeneous landscapes (2008, neutral): “2004; Urban 2005; Cushman et al .”
Landscape genetics of the Alpine newt (Mesotriton alpestris) inferred from a strip-based approach (2011, background): “Previous landscape genetics studies have frequently dealt with this issue by using Partial Mantel (Cushman et al. 2006; Spear et al. 2005), despite its controversy (Raufaste and Rousset 2001; Castellano and Balletto 2002; Rousset 2002).”
Scale dependent inference in landscape genetics (2010, neutral): “The predominant analytical approach to associate landscape patterns with gene flow processes uses pair-wise calculation of cost distances, using least cost paths (e.g. Cushman et al. 2006) or multi-path circuit approaches (McRae 2006).”