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Title: Latent spatial models and sampling design for landscape genetics

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

Date: 2016

Source: The Annals of Applied Statistics. 10(2): 1041-1062.

Publication Series: Scientific Journal (JRNL)

Description:

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.

Keywords: landscape genetics, sage grouse, optimal sampling

Publication Notes:

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Hanks, Ephraim M.; Hooten, Melvin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K. 2016. Latent spatial models and sampling design for landscape genetics. The Annals of Applied Statistics. 10(2): 1041-1062.

 


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