Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits
- Subject Areas
- Biosphere Interactions, Ecology, Environmental Sciences, Mathematical Biology
- Sharpe-Schoolfield, model, thermal response, trait, rate, physiology, temperature
- © 2017 Kontopoulos et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits. PeerJ Preprints 5:e3068v1 https://doi.org/10.7287/peerj.preprints.3068v1
There is currently unprecedented interest in quantifying variation in thermal physiology among organisms in order to understand and predict the biological impacts of climate change. A key parameter in this quantification of thermal physiology is the performance or value of a trait, across individuals or species, at a common temperature (temperature normalisation). An increasingly popular model for fitting thermal performance curves to data – the Sharpe-Schoolfield equation – can yield strongly inflated estimates of temperature-normalised trait values. These deviations occur whenever a key thermodynamic assumption of the model is violated, i.e. when the enzyme governing the performance of the trait is not fully functional at the chosen reference temperature. Using data on 1,758 thermal performance curves across a wide range of species, we identify the conditions that exacerbate this inflation. We then demonstrate that these biases can compromise tests to detect metabolic cold adaptation, which requires comparison of fitness or trait performance of different species or genotypes at some fixed low temperature. Finally, we suggest alternative methods for obtaining unbiased estimates of temperature-normalised trait values for meta-analyses of thermal performance across species in climate change impact studies.
This is a submission to PeerJ for review.
Supplementary material, mathematical derivations, and data sources.