Recently I was asked to write a review of the current state-of-the-art of model selection and Bayesian approaches to modelling in biogeography for the Geography Compass journal. The intended audience for the paper will be interested but non-expert, and the paper will “…summarize important research developments in a scholarly way but for a non-specialist audience”. With this in mind, the structure I expect I will aim for will look something like this:

i) Introduction to the general issue of model inference (i.e., “What is the best model to use?”). This section will likely discuss the modelling philosophy espoused by Burnham and Anderson and also highlight some of the criticisms of null-hypothesis testing using p-values. Then I might lead into possible alternatives (to standard p-value testing) such as:

ii) AIC approaches (to find the ‘best approximating model’)

iii) Bayesian approaches (including Bayesian Model Averaging, as I’ve discussed on this blog previously)

iv) Some applied examples (including my deer density modelling for example)

vi) A brief summary

I also expect I will try to offer some practical hint and tips, possibly using boxes with example R code (maybe for the examples in iv). Other published resources I’ll draw on will likely include the excellent books by Ben Bolker and Michael McCarthy. As things progress I may post more, and I’ll be sure to post again when the paper is available to read in full.