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

Benjamin M Bolker1, Mollie E Brooks, Connie J Clark

  • 1Department of Botany and Zoology, University of Florida, Gainesville, FL 32611-8525, USA. bolker@ufl.edu

Trends in Ecology & Evolution
|February 3, 2009
PubMed
Summary
This summary is machine-generated.

Ecologists and evolutionary biologists can use generalized linear mixed models (GLMMs) for nonnormal data with random effects. This review clarifies GLMM application, estimation, and inference for complex ecological and evolutionary analyses.

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Statistical Modeling

Background:

  • Nonnormal data, such as counts or proportions, are common in ecology and evolution.
  • Classical statistical methods are often inadequate for nonnormal data with random effects.
  • Generalized linear mixed models (GLMMs) offer a more flexible analytical approach.

Purpose of the Study:

  • To review the application and potential misuse of GLMMs in ecological and evolutionary research.
  • To discuss challenges in estimating GLMM parameters and performing statistical inference.
  • To provide best-practice recommendations for analyzing complex nonnormal data with random effects.

Main Methods:

  • Literature review of GLMM applications in ecology and evolution.
  • Discussion of estimation techniques for GLMM parameters.
  • Exploration of statistical inference methods, including hypothesis testing.

Main Results:

  • GLMMs are increasingly used but present challenges in complex scenarios.
  • Accurate estimation is possible for simple GLMMs, but inference remains difficult.
  • There is considerable uncertainty regarding best practices for GLMM implementation.

Conclusions:

  • Clarifying GLMM use is crucial for ecologists and evolutionary biologists.
  • Addressing challenges in estimation and inference is key to reliable data analysis.
  • Adopting best-practice procedures will enhance the rigor of ecological and evolutionary studies.