lab webpage: Evolutionary Quantitative Genetics
Evolutionary statistical quantitative genetics, or, analysis of longitudinal data from populations of unmanipulated animals
Some of the most valuable data for understanding how evolution works in natural populations is individual-based longitudinal data from pedigreed populations. Longitudinal data on individuals provides possibilities to link aspects of phenotype to life histories and fitness. Pedigree data allows inference of the genetic basis of variation in phenotypic traits, based on patterns of similarity of relatives.
With collaborators at the University of Edinburgh and elsewhere, a portion of my research revolves around the study of the selection and genetics of a range of traits in Soay sheep from St Kilda (pictured) and other long-term animal datasets from around the world.
Evolutionary genetic theory
I use analytical and computational approaches to understanding what patterns of genetic variation are expected in nature, and also of how to interpret observed patterns in microevolutionary parameters, including both aspects of genetics and selection. I have an ongoing interest in the patterns of genetic variation that are generated by complex landscape arrangements, especially in dendritic systems, which characterize all freshwater landscapes. I have recently been working on the interpretation of relationships between phenotypic traits and fitness mean in terms “chains of causation” in the context of characterizing the form of natural selection.
Software for empirical microevolutionary studies in nature
Analysis of data from natural populations is often very challenging. Datasets are often incomplete due to practical realities such as limited molecular information to resolve pedigrees, and/or imperfect detection of individuals for recording of life history information. I work on developing statistical tools to link fundamental evolutionary genetic theory to real data from the field. R packages include pedantics, and gsg.
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