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ABSTRACT Hybrid zones, where genetically distinct groups of organisms meet and interbreed, offer valuable insights into the nature of species and speciation. Here, we present a new R package,bgchm, for population genomic analyses of hybrid zones. This R package extends and updates the existingbgcsoftware and combines Bayesian analyses of hierarchical genomic clines with Bayesian methods for estimating hybrid indexes, interpopulation ancestry proportions, and geographic clines. Compared to existing software,bgchmoffers enhanced efficiency through Hamiltonian Monte Carlo sampling and the ability to work with genotype likelihoods combined with a hierarchical Bayesian approach, enabling inference for diverse types of genetic data sets. The package also facilitates the quantification of introgression patterns across genomes, which is crucial for understanding reproductive isolation and speciation genetics. We first describe the models underlyingbgchmand then provide an overview of the R package and illustrate its use through the analysis of simulated and empirical data sets. We show thatbgchmgenerates accurate estimates of model parameters under a variety of conditions, especially when the genetic loci analyzed are highly ancestry informative. This includes relatively robust estimates of genome‐wide variability in clines, which has not been the focus of previous models and methods. We also illustrate how both selection and genetic drift contribute to variability in introgression among loci and how additional information can be used to help distinguish these contributions. We conclude by describing the promises and limitations ofbgchm, comparingbgchmto other software for genomic cline analyses, and identifying areas for fruitful future development.
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Individual‐based landscape genomics for conservation: An analysis pipeline
Abstract Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non‐model systems has also enabled a shift away from population‐based sampling to individual‐based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual‐based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population‐based sampling to individual‐based sampling schemes. Here, we discuss the benefits of individual‐based sampling for conservation and describe how landscape genomic methods, paired with individual‐based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user‐friendly, open‐source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). Thealgatrpackage includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.
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- Award ID(s):
- 1845682
- PAR ID:
- 10512104
- Editor(s):
- Grueber, Catherine E
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- ISSN:
- 1755-098X
- Subject(s) / Keyword(s):
- conservation biology conservation genetics genetic diversity landscape genomics population structure spatial analysis
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/1755-0998.13884
