Latent spatial models and sampling design for landscape genetics

Ephraim M. Hanks, Mevin B. Hooten, Steven T. Knick, Sara J. Oyler-McCance, Jennifer A. Fike, Todd B. Cross, Michael K. Schwartz

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1041-1062
Number of pages22
JournalAnnals of Applied Statistics
Issue number2
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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