Models of evolution used in phylogenetic reconstruction make specific assumptions which (in their entirety, and globally applied) are ultimately wrong. They are also approximately right. What does this even mean? This week’s reading gets us into the notion of robustness of phylogenetic models to violations of their inherent assumptions. An important piece of the “which method should I use?” puzzle. Let’s see if we can identify other pieces too.
Nguyen, M.A.T., T. Gesell & A. von Haeseler. 2012. ImOSM: Intermittent evolution and robustness of phylogenetic methods. Molecular Biology and Evolution 29: 663-673. Available on-line here.
We had a lively Weekly Discussion of Kumar et al. 2012, and are staying with the general theme (hereby undemocratically coined) of “new insights in statistical phylogenetics/phylogenomics”. Models, biases, assumptions, data. Thus, for next week:
Wright, A.M. & D.M. Hillis. 2014. Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLoS ONE 9(10): e109210. Available on-line here.
After a long summer hiatus, our Weekly Discussion series resumes for the Fall semester of 2014 (this time also as a one-credit under-/graduate seminar course “Current Topics in Systematics”). On the menu this semester we have the theory and practice of Next Generation Sequencing, but the corresponding papers will have to wait a few weeks to make room for 2-3 unrelated topics on which new, intriguing papers have come out over the summer.
The first of these is on the philosophical correlates of statistical phylogenetic inference!
Barker, D. 2014. Seeing the wood for the trees: philosophical aspects of classical, Bayesian and likelihood approaches in statistical inference and some implications for phylogenetic analysis. Biology & Philosophy 29. (21 pp.). Available on-line here.
This week’s reading, diving into geographic patterns below the species level: Lemey, P., A. Rambaut, A.J. Drummond & M.A. Suchard. 2009. Bayesian phylogeography finds its roots. PLoS Computational Biology 5(9): e1000520. Available here.
Last week’s reading was a trip “back to the future” into MacArthur & Wilson (1967) style island biogeography – subtracting phylogeny and area cladograms from the equation and concentrating instead on island species richness and carrying capacities as a function of, well…(likely not just history and phylogeny). All this in a Bayesian framework. There was some question whether the current version of MrBayes can support such analyses. Next up for this week, more overview of parametric approaches: Ree, R.H. & I. Sanmartín. 2009. Prospects and challenges for parametric models in historical biogeographical inference. Journal of Biogeography 36: 1211–1220. Available here.
Continuing our theme of reviewing novel, parametric methods for historical biogeography, this week’s (lengthy) read is: Sanmartín, I., P. van der Mark & F. Ronquist. 2008. Inferring dispersal: a Bayesian, phylogeny-based approach to island biogeography, with special reference to the Canary Islands. Journal of Biogeography 35: 428–449. PDF available here.