landscape genetics

Environmental Studies II Lecture

A guest lecture in ENVS102: Environmental Studies II.

Landscape Genetics Course Feedback

If you took part in the Glasgow Landscape Genetics Course in March, I would appreciate any feedback you could provide so I can make the course better. Thank you.  

Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert-dwelling rodent

Gene flow in animals is limited or facilitated by different features within the landscape matrix they inhabit. The landscape representation in landscape genetics (LG) is traditionally modeled as resistance surfaces (RS), where novel optimization …

Jane Remfert, Doctoral Candidate

Jane Remfert has successfully completed the necessary steps to proceed to Doctoral Candidate by completing her written and oral defense and submitting her research proposal. Thank you to Drs. Eckert, Gough, Johnson, and Keyghobadi for their insightful comments and expertise in helping to shape a dynamic and exciting research project. Now, you just have to do it!

Editorial: The Least Cost Path From Landscape Genetics to Landscape Genomics: Challenges and Opportunities to Explore NGS Data in a Spatially Explicit Context

Talk at Temple!

Giving a talk up at Temple University, last seminar of the year but one I’ve been looking forward to giving for a while.

Volume 0 – On iBooks Store

Applied Population Genetics Textbook Release

I will be posting portions of all 10 chapters of my upcoming textbook, Applied Population Genetics, as early draft chapters to this website over the spring semester.

Population Graphs and Landscape Genetics

At the heart of the analyses of landscape genetics are isolation models seeking to explain either interindividual or interpopulation connectivity. These models use spatial, ecological, and topographic predictor variables measured between sites in an …

GStudio: An R Package for Spatial Analysis of Marker Data

This is the main package that provides data types and routines for spatial analysis of genetic marker data. The previous version is currently available on CRAN and you can install it rom within your R environtment by invoking the command install.packages(“gstudio”) If you want to keep up with the latest developments of this package, you can use the version found on GitHub. Install it from within R as: require(devtools) install_github(“dyerlab/gstudio”)