Estimating and comparing thermal performance curves

Research output: Contribution to journalArticle

196 Citations (Scopus)

Abstract

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
Volume31
Issue number7
DOIs
StatePublished - Oct 2006
Externally publishedYes

Fingerprint

Information Theory
Hot Temperature
Information theory
heat
Analysis of Variance
Analysis of variance (ANOVA)
analysis of variance
methodology

Keywords

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

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Physiology

Cite this

Estimating and comparing thermal performance curves. / Angilletta, Michael.

In: Journal of Thermal Biology, Vol. 31, No. 7, 10.2006, p. 541-545.

Research output: Contribution to journalArticle

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