LATENT SPATIAL MODELS AND SAMPLING DESIGN FOR LANDSCAPE GENETICS

• 成果类型: Article
• 署名作者: Hanks, Ephraim M. u; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Interior; United States Geological Survey; Colorado State University System; Colorado State University Fort Collins; United States Department of the Interior; United States Geological Survey; United States Department of the Interior; United States Geological Survey; United States Department of the Interior; United States Geological Survey; University of Montana System; University of Montana; United States Department of Agriculture (USDA); United States Forest Service
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/16-AOAS929
• 发表日期: 2016
• 页码: 1041-1062
• 关键词: greater sage-grouse; circuit-theory; inference; FLOW; connectivity; populations; prediction; DYNAMICS

摘要

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.