Sage Grouse Connectivity & Gene Flow Modeling
August 2012 - July 2014
- USGS Forest & Rangeland Ecosystem Science Center; Boise, ID
Problem statement: Wildlife genetic data are often collected, but the statistical methods to analyze such data parametrically are lacking. The greater sage-grouse is a species for which genetic data are collected, but sampling efforts are typically not optimal at the range-wide scale. Improved statistical models that can be useful to guide ongoing monitoring efforts. So What? Why this research matters: Improved statistical models for genetic data can be useful to improve our understanding of large-scale gene flow and guide ongoing monitoring efforts. Collaboration/Partners: This project is in collaboration with scientists at the U.S. Geological Survey and several western state agencies. Research That Informs Decisions: We developed formal parametric statistical models that allow for inference about gene flow using circuit theoretic concepts and environmental spatial data. Our new methodology guided the ongoing field data collection efforts to better understand gene flow for greater sage-grouse throughout the entire western U.S.
|Research Publications||Publication Date|
|Hanks, E.M. and M.B. Hooten. (2013). Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association, 108: 22-33.||March 2013|
|Hanks, E.M., M.B. Hooten, S.A. Knick, S.J. Oyler-McCance, J.A. Ficke, T.B. Cross, and M.K. Schwartz. (2016). Latent spatial models and sampling design for landscape genetics. Annals of Applied Statistics, 10: 1041-1062.||June 2016|