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

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          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.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Mixed effects models and extensions in ecology with R

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              Generalized linear mixed models: a practical guide for ecology and evolution.

              How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                23 May 2018
                2018
                : 6
                Affiliations
                [1 ] Institute of Zoology, Zoological Society of London , London, UK
                [2 ] Environment and Sustainability Institute, University of Exeter , Penryn, UK
                [3 ] Wildfowl and Wetlands Trust , Slimbridge, Gloucestershire, UK
                [4 ] Centre for Ecology and Conservation, University of Exeter , Penryn, UK
                [5 ] Department of Biology, University of Ottawa , Ottawa, ON, Canada
                [6 ] Department of Integrative Biology, University of Guelph , Guelph, ON, Canada
                [7 ] WildTeam Conservation , Padstow, UK
                Article
                4794
                10.7717/peerj.4794
                5970551
                © 2018 Harrison et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                Funding
                Funded by: Institute of Zoology Research Fellowship
                Funded by: NERC studentship
                Award ID: NE/H02249X/1
                Funded by: NERC studentship
                Award ID: NE/L501669/1
                Funded by: University of Exeter and the Animal and Plant Health as part of ‘Wildlife Research Co-Operative’
                Funded by: CONACYT (The Mexican National Council for Science and Technology)
                Funded by: SEP (The Mexican Ministry of Education)
                Funded by: Forestry Commission
                Funded by: NERC studentship
                Award ID: NE/L501669/1
                Xavier A. Harrison was funded by an Institute of Zoology Research Fellowship. David Fisher was funded by NERC studentship NE/H02249X/1. Lynda Donaldson was funded by NERC studentship NE/L501669/1. Beth S. Robinson was funded by the University of Exeter and the Animal and Plant Health Agency as part of ‘Wildlife Research Co-Operative’. Maria Correa-Cano was funded by CONACYT (The Mexican National Council for Science and Technology) and SEP (The Mexican Ministry of Education). Cecily Goodwin was funded by the Forestry Commission and NERC studentship NE/L501669/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Ecology
                Evolutionary Studies
                Statistics

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