Latent spatial models and sampling design for landscape genetics
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
Citation Information
Publication Year | 2016 |
---|---|
Title | Latent spatial models and sampling design for landscape genetics |
DOI | 10.1214/16-AOAS929 |
Authors | Ephraim M. Hanks, Mevin Hooten, Steven T. Knick, Sara J. Oyler-McCance, Jennifer A. Fike, Todd B. Cross, Michael K. Schwartz |
Publication Type | Article |
Publication Subtype | Journal Article |
Series Title | Annals of Applied Statistics |
Index ID | 70185004 |
Record Source | USGS Publications Warehouse |
USGS Organization | Coop Res Unit Seattle; Forest and Rangeland Ecosystem Science Center |