Technological wonders such as next generation sequencing mean that we can now, in principle, obtain SNP (single nucleotide polymorphism) data from multiple individuals in multiple species. This promises enormous benefits for population genetic and phylogenetic analysis, particularly of closely related or poorly resolved species. My interest is in how to analyse these data effectively and responsibly. We have developed an algorithm which estimates species trees, divergence times, and population sizes from independent (binary) makers such as well spaced SNPs. The method is based on coalescent theory (like the BEAST software), though it uses mathematical trickery to avoid having to consider all the possible gene trees. As a `full likelihood' method, it should be more accurate than alternative FST based approaches. I'll talk about our experiences applying this method to AFLP data from alpine plants, and some recent discoveries about the usefulness (or uselessness) of SNP data for estimating population sizes.
Bio: David did his PhD with Mike Steel at the University of Canterbury. He had postdocs with David Sankoff (Montreal) and Olivier Gascuel (Montpellier), before taking up a position at McGill. After tenure, he moved back to NZ for positions at the University of Auckland, and more recently Otago, which is in Dunedin in the South Island of NZ.