PUTTING THE R IN EVOLUTIONARY MODELING

Review of: Roff, D.A. 2010. Modeling Evolution: An Introduction to Numerical Methods. Oxford University Press, Oxford. ISBN: 9780199571147. $75.00 With the advent of easily available, powerful computing plat? forms, and the increasing number of data sets that require com? plex analyses, evolutionary biologists have a growing need for enhanced numerical and theoretical skills. Whether or not a sci? entist finds him or herself developing new theory, a good grasp of existing theoretical models and of how these models can be applied are important tools for every scientist. In his recent book, Modeling Evolution: An Introduction to Numerical Methods, Derek Roff provides a practical, hands-on treatment of some of the most basic and flexible approaches to theoretical model analysis, with a focus on implementation. Roff s text joins recent books that take advantage of the widespread availability of R, an open-source software environ? ment that compiles and runs on a wide variety of platforms, in? cluding UNIX, Windows, and MacOS. Most of these books have focused on mathematical models in the field of ecology, rather than evolution (Bolker 2008; Stevens 2009); thus, while they are useful to evolutionary researchers, a book focusing on more ex? plicitly evolutionary examples is a welcome addition. Roff states in his introduction that the book is aimed at graduate students and others wanting an introduction to the development and analysis of computer models of evolution. Roff s book differs from currently available texts on evolu? tionary models by providing detailed examples in the form of imbedded code for R and also for MATLAB, a commercially available numerical computing environment. By tying the exam? ples closely to the specific development platforms, the reader is provided with easily understandable, hands-on examples to fol low. R provides the additional benefits of being both free (open source) and widely available and of being already in extensive use for statistical applications. It is likely that many potential readers and users of the book will be familiar with R. Or perhaps, like this reviewer, they may have found themselves in the position of "intending-to-get-around-to-learning" R?the book provides a perfect excuse to jump in, and enough details to quickly find one's way around. Another clear advantage of this approach is that almost all of the figures in the text are generated from code that is included? allowing the reader to generate the same figures, and to easily learn how to create similar figures for his or her own problems. Re-creating the figures as one works through the text serves as an excellent tutorial on both creating figures in R and general methods for numerical visualization. One disadvantage of relying on platform-specific examples, however, is that the examples are overall less general; this ap? proach runs the risk that the reader would not truly understand the underlying model, but will rather focus on the details of the specific implementation. In some ways, a very detailed exam? ple approach, rather than a more general analytical approach, requires that the reader be already somewhat familiar with the analytical details of the model. There have been several recent texts that focus instead on an analytical approach to evolution? ary theory. Two examples include Otto and Day's A Biologist's Guide to Mathematical Modeling (2007) and Logan and Wolesen sky's Mathematical Methods in Biology (2009). Otto and Day's book takes a more basic and analytical approach to mathemati? cal modeling, introducing both the biological models and a re? view of the mathematical tools needed to develop and understand them. (Many of these mathematical details can be found in the