Book review: Bayesian Methods for Ecology

Review of Bayesian Methods for Ecology (NHBS) by Mick McCarthy

I've been meaning to write this for a couple of weeks, but I've either been too busy, or the moment I sit down to write it, a cat appears and sits on the keyboard. Well, I sorted the cat problem: he leapt up, I cut his nails.

So, the title of the book is fairly explanatory: it's about Bayesian methods for ecology. It's clearly aimed at ecologists who are not trained in statistics, but who need to use statistical methods. Mick spends a lot of the first half of the book giving the background to Bayesian methods, justifying their use, and criticising the use of hypothesis testing. He then moves on to describing standard models (regression and ANOVA) and how to fit them in a Bayesian way. The final part of the book consists of case studies using Bayesian methods, showing how they work in practice.

On the whole, I really like the book: it provides a simple, easy to follow, explanation of what Bayesian methods are, and how to use BUGS to fit simple models. the latter point is important, as it means that ecologists can see how to use the methods in practice, and the code and data are available on the web.

One thing I did like about the book is its emphasis on using information from outside the data to improve the estimation. This is an aspect if Bayesian methods that I use less than I should, but is perhaps particularly important in practical conservation problems, where there is a lot of background information, and the aim is not to demonstrate some theory (where informative priors can bias the demonstration). If this book encourages scientists to use Bayesian method for problems where several strands of information have to be synthesised, then it will have done a useful service.

I have a couple of criticisms. The first is that I feel the strength of the Bayesian approach is in the way it can handle complex models. This relies on a hierarchical scheme for modelling data. Although this is mentioned, I would have liked to have seem more on it: I think a whole chapter would have been worth aiming for (and possible combining the regression and ANOVA chapters: they're really the same thing). My second criticism is over the excessive use of DIC. This is a criterion for comparing how adequately different models fit to the data. This is really a philosophical complaint: the problem with using criteria like DIC is similar to one problem with hypothesis testing: it says something about the statistical properties of the models, but nothing about the substansive, ecological, properties. Given enough data, DIC will show that the more complex model is better. It will not show whether the extra effects are important in any real way. I would rather see a greater emphasis on examining the parameters, and using them to decide if the more complex model explains anything: does removing the parameters reduce the predictive ability substansively (rather than statistically)?

So, the book isn't perfect, but I would still recommend it to ecologists wanting to understand the basics of Bayesian methods: not only does it give a glimpse of what they can do, but it also allows them to do it. I hope it will help ecologists (and others!) get over the first few steps to doing Bayesian analyses, by giving a simple explanation, and code to run (and change!) on simple problems. After that, what follows is the same, but bigger. Once the first hurdle is passed, more advanced problems can be tackled with the help of Andrew Gelman's latest book, so it should not be too difficult to progress to the complex models of the sort I find myself working on.

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