Open Access
June 2016 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
Ann. Appl. Stat. 10(2): 1041-1062 (June 2016). DOI: 10.1214/16-AOAS929


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.


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Ephraim M. Hanks. Mevin B. Hooten. Steven T. Knick. Sara J. Oyler-McCance. Jennifer A. Fike. Todd B. Cross. Michael K. Schwartz. "Latent spatial models and sampling design for landscape genetics." Ann. Appl. Stat. 10 (2) 1041 - 1062, June 2016.


Received: 1 December 2014; Revised: 1 March 2016; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625680
MathSciNet: MR3528371
Digital Object Identifier: 10.1214/16-AOAS929

Keywords: Landscape genetics , optimal sampling , sage grouse

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 2 • June 2016
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