Linear mixed effects models and generalized linear mixed effects models (GLMMs), have increased in popularity in the last decade ( Zuur et al., 2009 Bolker et al., 2009). 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 accordingly ( Bolker et al., 2009). Often, 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). The availability of novel and sophisticated statistical techniques means we are better equipped than ever to extract signal from noisy biological data, but it remains challenging to know how to apply these tools, and which statistical technique(s) might be best suited to answering specific questions ( Kass et al., 2016). In recent years, the suite of statistical tools available to biologists and the complexity of biological data analyses have grown in tandem ( Low-Décarie, Chivers & Granados, 2014 Zuur & Ieno, 2016 Kass et al., 2016). 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.
We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding.
We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology.
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. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. 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 use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data.
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