Performance of the SSI development function compared with 33 other functions applied to 79 arthropod species' datasets.

The development rates of arthropods are temperature-dependent. Studies aiming to predict the dynamics of arachnid, crustacean, and insect populations in nature often require the derivation of development functions representing this phenomenon. A previous study (Quinn, B.K., 2017, J. Therm. Biol. 63, 65-77) identified 33 development functions commonly used in past studies on temperature-dependent development of arthropods, and illustrated that: (1) most of 99 past studies only applied one or few (2-5) development functions to their data without considering others; and (2) most of a subset of 79 studies' data were not fit with the actual best function for them, resulting in sometimes substantial differences in model performance and predictive ability. However, that study did not test the class of development functions based on theoretical enzyme thermodynamics, including the Sharpe-Schoolfield-Ikemoto (SSI) function. Herein, the meta-analyses done in that previous study were redone, after fitting all 79 reanalyzed datasets with the SSI function. Estimates of the intrinsic optimum temperature (TΦ) for development of each tested species were also derived using the SSI function and compared among taxa. Including the SSI function in analyses did not change the conclusions of the previous study concerning development function usage, choice, and consequences. Notably, the SSI function performed as well as or relatively better than other functions of comparable or lower complexity in terms of R2, AICC-based rankings, ΔAICC values, and prediction errors, which may recommend its more widespread use in future studies. Overall differences in TΦ were found among arthropod subphyla, as well as between most species pairs. Most TΦ estimates produced herein were novel, and could be used to make inferences about or comparisons among arthropod taxa in future studies.

Use and misuse of temperature normalisation in meta-analyses of thermal responses of biological traits.

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.

Measuring the plasticity of developmental rate across insect populations: comment on Rocha and Klaczko (2012).

Rocha and Klaczko emphasize the general complexity of reaction norm shape and caution that ignoring such complexity can be misleading when forcing nonlinear reaction norms into linear shapes. They refer to our article on differences in plasticity of Drosophila serrata populations along a latitudinal gradient as an example of a misleading simplifying approach. However, their claim that an artifact is introduced into our analyses by calculating developmental rate as the reciprocal of development time (rate = time(-1)) is based on a misunderstanding of the mathematical properties of the thermal developmental rate reaction norm. Here we discuss why developmental rate is a suitable measure for our study and under which circumstances it is appropriate to describe developmental rate by a linear model.