Applications of landscape genetics in conservation biology: concepts and challenges

By Gernot Segelbacher, Samuel A. Cushman, B. Epperson, Marie-Josée Fortin, Olivier Francois, Olivier J. Hardy, R. Holderegger, P. Taberlet, L. Waits, S. Manel. Conservation Genetics, 2010

394 citations recorded by the source corpus.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/d48109bf1d6947b83d555f94d1ed04b64ff4c926

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Citations in the corpus

Effects of sample size, number of markers, and allelic richness on the detection of spatial genetic pattern (2012, background): “The sensitivity of landscape genetic analyses to variation in the sampling scheme used to obtain the genetic data remains largely unexplored (Balkenhol et al. 2009; Segelbacher et al. 2010).”
Estimating effective landscape distances and movement corridors: Comparison of habitat and genetic data (2015, methodology): “Individual-based analysis comparing pairwise genetic distances to pairwise effective distances under multiple landscape resistance hypothesis are a powerful tool for supporting conservation efforts (Cushman et al. 2006, 2013a, Epps et al. 2007, Segelbacher et al. 2010).”
Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics (2013, background): “Landscape genetics provides a powerful approach to evaluate the effects of multiple landscape features on population connectivity [1–12].”
Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate (2013, background): “Traditionally, landscape genetics has used selectively neutral genetic markers and focused on the impact of landscape features in shaping movement of animals, gene movement of plants via seed and pollen, and geographic patterns of genetic variation, as discussed in numerous reviews on the topic (Bal”
Scale dependent inference in landscape genetics (2010, neutral): “There has been no formal evaluation of how changing grain, extent or thematic resolution of the underlying landscape resistance model affects ability to correctly and reliably identify the driving causal process (Balkenhol et al. 2009; Segelbarcher et al. 2010).”