Scale dependent inference in landscape genetics
By Samuel A. Cushman, Erin L. Landguth. Landscape Ecology, 2010
174 citations recorded by the source corpus.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/1e9ffcc886c8eda92d63320a696ecec35611b094
How researchers see this paper
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In Genetics
- Samuel A. Cushman
- Erin L. Landguth
- T. Wasserman
- David O. Wallin
- M. D. Schwartz
- Jesse S. Lewis
- Marie-Josée Fortin
- K. Zeller
Citations in the corpus
- Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho (2010, background): “For a given individual at a given location in a landscape, the probability of dispersing to and selecting a particular mate can be seen as a function of the distance to that mate in Euclidean or cost units (e.g. Landguth and Cushman 2010).”
- Isolation by environment (2014, unknown): “While spatial variation and scale have been identified as important factors in landscape genetic analysis (Cushman & Landguth 2010), the implications of temporal variation have not yet been thoroughly investigated, although some recent studies provide indications of their importance (Crispo &…”
- Evaluating population connectivity for species of conservation concern in the American Great Plains (2013, unknown): “Resampling with bilinear interpolation retains the net resistant effect across the merged pixels and is an appropriate method to coarsen resistance maps while preserving the spatial pattern of resistance to movement (Cushman and Landguth 2010).”
- Multi-taxa population connectivity in the Northern Rocky Mountains (2012, background): “Cushman and Landguth (2010) showed that coarsening the pixels size in floating point resistance grids has little effect on the strength of landscape genetic relationships, suggesting that connectivity models are quite robust to coarsening of pixel grain.”
- Circuit Theory and Model-Based Inference for Landscape Connectivity (2013, unknown): “While the circuit theory model of gene flow (McRae 2006) is formulated at a population level, with the assumption that each grid cell on a landscape contains a subpopulation, it is often applied to individual-level genetic data (e.g., Cushman et al. 2006; Cushman and Landguth 2010).”