Movement behavior explains genetic differentiation in American black bears

By Samuel A. Cushman, Jesse S. Lewis. Landscape Ecology, 2010

222 citations recorded by the source corpus.

Subfields: Ecology, Genetics, Small Animals

View Paper: https://www.semanticscholar.org/paper/bc1c75975554009de83a466dfcec7303df39a2f3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Genetics

In Small Animals

Citations in the corpus

Evaluating population connectivity for species of conservation concern in the American Great Plains (2013, unknown): “We recommend further investment of resources to test and validate the resistance maps produced by this analysis using movement (e.g. Short Bull et al. 2011; Cushman and Lewis 2010; Reding et al. 2013) and landscape genetic methods (e.g. Cushman et al. 2006; Wasserman et al. 2010; Shirk et al. 2010).”
Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus) (2013, unknown): “We used a case–control design common in resource selection studies, focusing on entire paths as the unit of analysis (Bruggeman et al. 2007; Cushman and Lewis 2010; Cushman et al. 2011) as advocated by Zeller et al.”
Placing linkages among fragmented habitats: do least‐cost models reflect how animals use landscapes? (2011, methodology): “…study grain was typically dictated by freely available remotely sensed data (see Table S1 Supporting information) rather than species perceptions of landscape features (Cushman & Lewis 2010; see Appendix S2 in Supporting information for recommendations on improving application of LCP analysis).”
Ecological connectivity research in urban areas (2015, background): “The success of these movements influences the survival of individuals and consequently the transfer of genes (e.g. Epps et al. 2005; Cushman & Lewis 2010), ultimately affecting population dynamics, the distribution of species and ecosystem functioning (Jeltsch et al. 2013).”
Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data (2015, background): “For example, a resistance surface that is supported by independent analysis of movement and genetic data (e.g., Cushman and Lewis 2010) is much more likely to be robust than one developed from a single empirical data set.”
Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data (2018, background): “These results are similar to what Cushman and Lewis (2010) and Cushman et al. (2014) found for black bears in northern Idaho.”
Estimating landscape resistance to movement: a review (2012, methodology): “In this case, alternative resistance surfaces were derived by experts a priori and the pathway data were used to select the surface that best discriminated between observed and random paths (Cushman and Lewis 2010).”
Biological corridors and connectivity (2013, methodology): “Cushman & Lewis (2010) used conditional logistic regression to predict landscape resistance based on black bear GPS telemetry data in the same landscape.”
Circuit‐theory applications to connectivity science and conservation (2018, background): “Future applications of circuit theory could account for temporal variation in dispersal rates (Anderson et al. 2010; Cushman & Lewis 2010) and predicting patterns of variation at adaptive loci (e.g., Creech et al. 2017).”
Multispecies assessment of core areas and connectivity of desert carnivores in central Iran (2018, methodology): “…the predicted corridors, including monitoring movements of a large number of individuals using camera- trapping and satellite tacking (e.g., Cushman & Lewis, 2010; Elliot et al., 2014), using genetic mark–recapture and landscape genetic analyses (e.g., Shirk, Wallin, Cushman, Rice, &…”