Power analysis for generalized linearmixedmodels in ecology and evolution

1. ‘Will my study answer my research question? ’ is the most fundamental question a researcher can ask when designing a study, yet when phrased in statistical terms – ‘What is the power of my study? ’ or ‘How precise will my parameter estimate be? ’ – few researchers in ecology and evolution (EE) try to answer it, despite the detrimen-tal consequences of performing under- or over-powered research. We suggest that this reluctance is due in large part to the unsuitability of simple methods of power analysis (broadly defined as any attempt to quantify pro-spectively the ‘informativeness ’ of a study) for the complex models commonly used in EE research.With the aim of encouraging the use of power analysis, we present simulation from generalized linearmixedmodels (GLMMs) as a flexible and accessible approach to power analysis that can account for random effects, overdispersion and diverse response distributions. 2. We illustrate the benefits of simulation-based power analysis in two research scenarios: estimating the preci-sion of a survey to estimate tick burdens on grouse chicks and estimating the power of a trial to compare the effi-cacy of insecticide-treated nets in malaria mosquito control. We provide a freely availableR function, sim.glmm, for simulating fromGLMMs. 3. Analysis of simulated data revealed that the effects of accounting for realistic levels of random effects and overdispersion on power and precision estimates were substantial, with correspondingly severe implications fo