Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

By T. Wasserman, Samuel A. Cushman, M. D. Schwartz, David O. Wallin. Landscape Ecology, 2010

169 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1da467e1bc7cc49a703afca34b7daf54add4abad

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Citations in the corpus

Movement behavior explains genetic differentiation in American black bears (2010, methodology): “Recent work has refined and extended these approaches by incorporating multiscale modeling and improved model selection approaches (Shirk et al. 2010; Wasserman et al. 2010).”
Estimating landscape resistance to movement: a review (2012, methodology): “In these multi-variable studies, with one exception (Wasserman et al. 2010), variables were combined after analyzing the variables individually or fit simultaneously in the statistical model (e.”
Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics (2013, methodology): “Wasserman et al. [11] expanded the causal modeling framework [24,25] to provide more robust landscape genetic inference.”
Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus) (2013, unknown): “Even within a population, recent studies have shown that habitat fragmentation can have a substantial effect on landscape connectivity and the dispersal of animals, reducing gene flow and generating fine-scale patterns of genetic differentiation (Coulon et al. 2004; Wasserman et al. 2010).”
GENETIC ISOLATION BY ENVIRONMENT OR DISTANCE: WHICH PATTERN OF GENE FLOW IS MOST COMMON? (2014, background): “In addition, gene flow might increase or decrease with climate change, depending on how climate change affects suitable habitat (Wasserman et al. 2010; Olsen et al. 2011).”
Evaluating population connectivity for species of conservation concern in the American Great Plains (2013, unknown): “2013) and landscape genetic methods (e.g. Cushman et al. 2006; Wasserman et al. 2010; Shirk et al. 2010).”
Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data (2015, background): “This approach essentially implies that animal movements are influenced by the same environmental factors as habitat selection (e.g., Chetkiewicz et al. 2006, Wasserman et al. 2010, Zeller et al. 2012).”
Connectivity of the global network of protected areas (2016, background): “…have reported dispersal plasticity (i.e. lower sensitivity to land cover matrix characteristics and ability to use suboptimal habitats when dispersing than resident individuals) in mammals (e.g. Palomares et al., 1999; Wasserman et al., 2010; Reding et al., 2013; Mateo-S anchez et al., 2015).”