A brief introduction to mixed effects modelling and multi-model inference in ecology

By X. Harrison, Lynda Donaldson, Maria Eugenia Correa-Cano, J. Evans, David N. Fisher, Cecily E. D. Goodwin, Beth S Robinson, David Hodgson, Richard Inger. PeerJ, 2018

1,831 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/83125c947768e4627692582b797c2bee70223ecc

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Citations in the corpus

Robustness of linear mixed‐effects models to violations of distributional assumptions (2020, background): “The link function and the specific error distributions cause additional complications and need to be chosen appropriately (Bolker et al., 2009; Harrison et al., 2018).”
Temperature and rainfall impacts on robusta coffee bean characteristics (2021, background): “…models efficiently account for the influence of these grouping factors (e. g. site and year) and eliminate many problems associated with spatial and temporal autocorrelation, pseudo-replication and also reduce the risk of Type I and II errors (Bolker et al., 2009; Harrison et al., 2018).”
Human perturbations reduce dung beetle diversity and dung removal ecosystem function (2021, methodology): “…we included sampling zone (with six levels obtained from the intersection of the three study areas and the two forest types) as a single random factor to account for the non- independence in environmental conditions of the experimental units (refer Harrison et al., 2018; Pinheiro & Bates, 2000).”
Conducting and interpreting fish telemetry studies: considerations for researchers and resource managers (2019, background): “Common sources of this issue in telemetry datasets are zero-inflation and outliers in the data (Brooks et al. 2017b; Harrison et al. 2018).”
Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R (2020, methodology): “…resources aimed at using (G)LMMs (e.g. Baayen et al., 2008; Bates, Mächler, Bolker, & Walker, 2014; DeBruine & Barr, 2021; Goldstein, 2007; Harrison et al., 2018), but far less guidance is available for experimental psychologists who want to estimate power for (G)LMMs (Brysbaert &…”
The consequences of checking for zero‐inflation and overdispersion in the analysis of count data (2019, methodology): “While simulation is considered by some to be a powerful tool for assessing model fit, it remains ‘rarely used’ (Harrison et al., 2018).”
Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: a PRISMA extension (2021, methodology): “For model selection and justification, principles from ordinary regression analyses apply to meta-regressions too (Gelman & Carlin, 2014; Harrison et al., 2018; Meteyard & Davies, 2020).”