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Latent spatial models and sampling design for landscape genetics

Annals of Applied Statistics

By:
, ORCID iD , ORCID iD , ORCID iD , , , and
https://doi.org/10.1214/16-AOAS929

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Abstract

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.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Latent spatial models and sampling design for landscape genetics
Series title:
Annals of Applied Statistics
DOI:
10.1214/16-AOAS929
Volume:
10
Issue:
2
Year Published:
2016
Language:
English
Publisher:
Project Euclid
Contributing office(s):
Coop Res Unit Seattle, Forest and Rangeland Ecosystem Science Center
Description:
22 p.
First page:
1041
Last page:
1062