Journal
EVOLUTION
Volume 67, Issue 11, Pages 3258-3273Publisher
WILEY
DOI: 10.1111/evo.12193
Keywords
Isolation by distance; isolation by ecology; landscape genetics; partial Mantel test
Categories
Funding
- National Science Foundation [1262645]
- National Institutes of Health Ruth L. Kirschstein NRSA fellowship [F32GM096686]
- Sloan Foundation
- National Science Foundation GRFP [1148897]
- Direct For Biological Sciences [1262327] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1262645] Funding Source: National Science Foundation
- Div Of Biological Infrastructure [1262327] Funding Source: National Science Foundation
Ask authors/readers for more resources
Populations can be genetically isolated both by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte data sets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available