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Abstract

Multi-model inference (MMI) aims to contribute to the production of scientific knowledge by simultaneously comparing the evidence data provide for multiple hypotheses, each represented as a model. With roots in the method of ‘multiple working hypotheses’, MMI techniques have been advocated as an alternative to null-hypothesis significance testing. In this paper, we review two complementary MMI techniques – model selection and model averaging – and highlight examples of their use by biogeographers. Model selection provides a means to simultaneously compare multiple models to evaluate how well each is supported by data, and potentially to identify the best supported model(s). When model selection indicates no clear ‘best’ model, model averaging is useful to account for parameter uncertainty. Both techniques can be implemented in information-theoretic and Bayesian frameworks and we outline the debate about interpretations of the different approaches. We summarise recommendations for avoiding philosophical and methodological pitfalls, and suggest when each technique is best used. We advocate a pragmatic approach to MMI, one that emphasises the ‘thoughtful, science-based, a priori’ modelling that others have argued is vital to ensure valid scientific inference.