A brief introduction to mixed effects modelling and multi-model inference in ecology.
Xavier A. Harrison,Lynda Donaldson,Lynda Donaldson,Maria Eugenia Correa-Cano,Julian C. Evans,Julian C. Evans,David N. Fisher,David N. Fisher,Cecily E. D. Goodwin,Beth S. Robinson,David J. Hodgson,Richard Inger +11 more
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TLDR
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.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.read more
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Robustness of linear mixed-effects models to violations of distributional assumptions
Holger Schielzeth,Niels Jeroen Dingemanse,Shinichi Nakagawa,David F. Westneat,Hassen Allegue,Céline Teplitsky,Denis Réale,Ned A. Dochtermann,László Zsolt Garamszegi,Yimen G. Araya-Ajoy +9 more
Posted ContentDOI
Violating the normality assumption may be the lesser of two evils
Ulrich Knief,Wolfgang Forstmeier +1 more
TL;DR: It is argued that violating the normality assumption bears risks that are limited and manageable, while several more sophisticated approaches are relatively error prone and difficult to check during peer review.
Journal ArticleDOI
Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: a PRISMA extension.
Rose E. O'Dea,Malgorzata Lagisz,Michael D. Jennions,Julia Koricheva,Daniel W. A. Noble,Daniel W. A. Noble,Timothy H. Parker,Jessica Gurevitch,Matthew J. Page,Gavin B. Stewart,David Moher,Shinichi Nakagawa +11 more
TL;DR: The PRISMA-EcoEvo project as mentioned in this paper provides guidelines for the ecology and evolutionary biology community to facilitate transparent and comprehensively reported systematic reviews and meta-analyses.
Journal ArticleDOI
Violating the normality assumption may be the lesser of two evils
Ulrich Knief,Wolfgang Forstmeier +1 more
TL;DR: In this article, Monte Carlo simulations were used to explore the pros and cons of fitting Gaussian models to non-normal data in terms of risk of type I error, power and utility for parameter estimation.
Journal ArticleDOI
Ignoring non-English-language studies may bias ecological meta-analyses.
Ko Konno,Munemitsu Akasaka,Chieko Koshida,Naoki Katayama,Noriyuki Osada,Rebecca Spake,Tatsuya Amano,Tatsuya Amano +7 more
TL;DR: It is suggested that ignoring non‐English‐language studies may bias outcomes of ecological meta‐analyses, due to systematic differences in study characteristics and effect‐size estimates between English‐ and non-English languages.
References
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Multimodel Inference Understanding AIC and BIC in Model Selection
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