Estimating and comparing thermal performance curves

Research output: Contribution to journalArticlepeer-review

365 Scopus citations


I show how one can estimate the shape of a thermal performance curve using information theory. This approach ranks plausible models by their Akaike information criterion (AIC), which is a measure of a model's ability to describe the data discounted by the model's complexity. I analyze previously published data to demonstrate how one applies this approach to describe a thermal performance curve. This exemplary analysis produced two interesting results. First, a model with a very high r2 (a modified Gaussian function) appeared to overfit the data. Second, the model favored by information theory (a Gaussian function) has been used widely in optimality studies of thermal performance curves. Finally, I discuss the choice between regression and ANOVA when comparing thermal performance curves and highlight a superior method called template mode of variation. Much progress can be made by abandoning traditional methods for a method that combines information theory with template mode of variation.

Original languageEnglish (US)
Pages (from-to)541-545
Number of pages5
JournalJournal of Thermal Biology
Issue number7
StatePublished - Oct 2006
Externally publishedYes


  • AIC
  • Model selection
  • Temperature
  • Thermal optimum
  • Thermal performance curves

ASJC Scopus subject areas

  • Biochemistry
  • Physiology
  • General Agricultural and Biological Sciences
  • Developmental Biology


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