Greater sage-grouse genetic data and R code for evaluating conservation translocations in the northwestern United States, 1992–2021

Dates

Publication Date: 2024-02-06
Start Date: 1992
End Date: 2021

Citation

Zimmerman, S.J., Fike, J., Cornman, R.S., Schroeder, M.A., Aldridge, C., and Oyler-McCance, S.J., 2024, Greater sage-grouse genetic data and R code for evaluating conservation translocations in the northwestern United States, 1992–2021: U.S. Geological Survey data release, https://doi.org/10.5066/P13UWMYL.

Summary

Conservation translocations are a common wildlife management tool that can be difficult to implement and evaluate for effectiveness. Genetic information can provide unique insight regarding local impact of translocations (e.g., presence and retention of introduced genetic variation) and identifying suitable source and recipient populations (e.g., adaptive similarity). We developed two genetic data sets and wrote statistical code to evaluate conservation translocation effectiveness into the isolated northwestern region of the greater sage-grouse (Centrocercus urophasianus) distribution and to retrospectively evaluate adaptive divergence among source and recipient populations. Our first data set was microsatellite-based and derived from [...]

Summary

Conservation translocations are a common wildlife management tool that can be difficult to implement and evaluate for effectiveness. Genetic information can provide unique insight regarding local impact of translocations (e.g., presence and retention of introduced genetic variation) and identifying suitable source and recipient populations (e.g., adaptive similarity). We developed two genetic data sets and wrote statistical code to evaluate conservation translocation effectiveness into the isolated northwestern region of the greater sage-grouse (Centrocercus urophasianus) distribution and to retrospectively evaluate adaptive divergence among source and recipient populations. Our first data set was microsatellite-based and derived from biological samples (feathers, tissue, and blood) collected from the translocation source populations and the northwestern recipient populations (in Washington state) before and after translocation. These data were used to evaluate neutral change in genetic variation resulting from translocation efforts. We wrote code for statistical analyses to evaluate two things in our microsatellite-based data. First, we developed a simulation model to predict the genetic effect of conservation translocations and compare the predictions to what was observed. Second, we developed a statistical model to estimate the probability that individuals sampled post-translocation are the offspring of two individuals from the same population or from individuals from two distinct populations. Our second data set was whole-genome sequencing data (derived from tissue and blood samples) for the source and Washington populations prior to translocation efforts. These data were used to characterize genome-wide adaptive divergence patterns that may influence translocation outcomes.

Contacts

Point of Contact :: Shawna J Zimmerman
Originator :: Shawna J Zimmerman, Jennifer M Fike, Robert S Cornman, Michael A Schroeder, Cameron L Aldridge, Sara J Oyler-McCance
Metadata Contact :: GS-RMA-FORT Data Management
Publisher :: U.S. Geological Survey
Distributor :: U.S. Geological Survey - ScienceBase
SDC Data Owner :: Fort Collins Science Center
USGS Mission Area :: Ecosystems

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wa_grsg_translocation_evaluation_project.xml Original FGDC Metadata	View	21.05 KB	application/fgdc+xml
Genetic_data.zip		20.34 KB	application/zip
R_code_and_test_data.zip		25.18 KB	application/zip

Related External Resources

Type: Related Primary Publication

Zimmerman, S.J., Aldridge, C.L., Schroeder, M.A., Fike, J.A., Cornman, R.S., and Oyler‐McCance, S.J., 2024, The potential influence of genome‐wide adaptive divergence on conservation translocation outcome in an isolated greater sage‐grouse population: Conservation Biology, v. 38, no. 4, https://doi.org/10.1111/cobi.14254.	https://doi.org/10.1111/cobi.14254

Purpose

Data were collected and code was written to explore changes in genetic diversity and population structure before and after conservation translocations, and to better understand patterns of adaptive divergence. We included simulated test data with our R code to clearly demonstrate the functionality.

Greater sage-grouse genetic data and R code for evaluating conservation translocations in the northwestern United States, 1992–2021

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