Abstract
The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
Highlights
In recent years, the suite of statistical tools available to biologists and the complexity of biological data analyses have grown in tandem (Low-Decarie, Chivers & Granados, 2014; Zuur & Ieno, 2016; Kass et al, 2016)
Simple analyses will be sufficient (Murtaugh, 2007), but more complex data structures often require more complex methods such as linear mixed effects models (LMMs) (Zuur et al, 2009), generalized additive models (Wood, Goude & Shaw, 2015) or Bayesian inference (Ellison, 2004). Both accurate parameter estimates and robust biological inference require that ecologists be aware of the pitfalls and assumptions that accompany these techniques and adjust modelling decisions (Bolker et al, 2009)
We address methods of model selection, and discuss the relative merits and potential pitfalls of using information theory (IT), AIC and multi-model inference in ecology and evolution
Summary
The suite of statistical tools available to biologists and the complexity of biological data analyses have grown in tandem (Low-Decarie, Chivers & Granados, 2014; Zuur & Ieno, 2016; Kass et al, 2016). Examples include: (i) failure to recognise non-independence caused by nested structures in the data e.g. multiple clutch measures from a single bird; (ii) failing to specify random slopes to prevent constraining slopes of predictors to be identical across clusters in the data (see Barr et al, 2013); and (iii) testing the significance of fixed effects at the wrong ‘level’ of hierarchical models that leads to pseudoreplication and inflated Type I error rates.
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