*Ecological Archives* E096-274-A2

P. S. Petraitis and S. R. Dudgeon. 2015. Variation in recruitment and the establishment of alternative community states. *Ecology* 96:3186–3196. http://dx.doi.org/10.1890/14-2107.1

Appendix B. Description of Burnham and Anderson’s approach for multimodel inference.

Burnham and Anderson’s (2002) approach relies on AIC differences, Akaike weights and evidence ratios to evaluate a group of competing models. AIC difference or Δi is the Akaike Information Criteria (AIC) for the *i*th model minus smallest AIC; this is a relative measure and models with Δi < 2 have substantial support. Akaike weights, *wi*, are the relative likelihood that the ith model is the best supported model given the data and the specified group of competing models. By definition, Δi = 0 for the best supported model and Σ*wi* =1. Evidence ratio is the comparison of two competing models and is *wi* / *wj*. This gives the relative odds of one model against another. For example, a ratio of six would suggest model *i* is six times more likely to be the best model than model *j*. Ideally, one would like to find a single model for which Δi < 2 and all evidence ratios are much larger than 1. In practice, usually a subset of models are equally plausible and when this occurred, we used model averaging in which calculation of the average parameter estimate is weighted by *wi*.

** **Literature cited

Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach. Second edition. Springer.