There is currently unprecedented interest in quantifying variation in thermal physiology
among organisms, especially 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 rate, 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 rate values. These deviations
occur whenever a key thermodynamic assumption of the model is violated, i.e., when
the enzyme governing the performance of the rate 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 rate performance of different
species or genotypes at some fixed low temperature. Finally, we suggest alternative
methods for obtaining unbiased estimates of temperature-normalised rate values for
meta-analyses of thermal performance across species in climate change impact studies.