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

Bibliographic Data

ID23367702
AuthorsXavier A Harrison (0000-0002-2004-3601, Zoological Society of London), Lynda Donaldson (0000-0002-1892-3941, University of Exeter), Maria Eugenia Correa-Cano (Environment and Sustainability Institute, University of Exeter, Penryn, UK), Maria Correa-Cano (0000-0002-2765-2792, University of Exeter), Julian Evans (0000-0001-7837-5927, University of Ottawa), David N Fisher (0000-0002-4444-4450, University of Exeter), Cecily Goodwin (0000-0003-0093-9838, University of Exeter), Cecily E D Goodwin (Environment and Sustainability Institute, University of Exeter, Penryn, UK), Beth S Robinson (0000-0001-7448-3875, University of Exeter), David J Hodgson (0000-0003-4220-2076, University of Exeter), Richard Inger (0000-0003-1660-3706, University of Exeter)
Year2018
Volume6
Pagese4794
Publication date2018-05-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePeerJ (JOURNAL)
Journal identifiersISSN: 2167-8359 • E-ISSN: 2167-8359
PublisherPeerJ (PUBLISHER)
DOI10.7717/peerj.4794
PMID29844961
OpenAlexW2783564415
LanguageEN
Citations received75
References cited83

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.

Bioinformatics · Biological data · Data mining · Data science · Inference · Machine learning · Management science · Model selection · Robustness (evolution) · Artificial Intelligence · Computer Science · Ecology · Ecology and Vegetation Dynamics Studies · Engineering · Species Distribution and Climate Change · Wildlife Ecology and Conservation

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Unique citing works75
Citations per year10,71
Citation span2019 - 2026 (8)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 68

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