A general theory for temperature-dependence in biology

Abstract

Temperature affects all biological rates and has far reaching consequences from bioengineering [1] to predicting ecological shifts under a changing climate [2-3], and more recently, to pandemic spread [4]. Temperature response in biological systems is characteristically asymmetric and nonlinear, with an exponential phase of increase followed by a concave up-ward or downward phase [5]. Current models for quantitatively describing the temperature response include simple but empirical equations (such as Arrhenius’) or models derived from first principles which are often overly complicated (i.e. with many parameters). Moreover, their theoretical framework does not include how parameters vary, nor their applicability across multiple scales and taxa, or whether they exhibit universality [1-7]. Here, we derive a new mechanistic, yet simple, model for the temperature dependence of biological rates based on the Eyring-Evans-Polanyi theory governing chemical reaction rates, which is applicable across all scales from the micro to the macro. Assuming only that the conformational entropy of molecules changes with temperature, we derive a model for the temperature dependence which takes the form of an exponential function modified by a power-law. Data for a wide variety of biological rates from molecular to ecological scales and across multiple taxonomic groups agree well with our predictions. Furthermore, our framework predicts values for the thermodynamic parameters, and leads to a single parameterless universal scaling curve on which data across all scales and taxa collapse.

Speed it up: How temperature drives toxicokinetics of organic contaminants in freshwater amphipods

“…The convergence of test temperatures with an exceedance of physiological optimum temperatures may result in decreasing respiration rates (Galic & Forbes, 2017) and enzymatic activity (Jakob et al, 2021) in amphipods. In such cases, non‐classic Arrhenius relationships may apply (Arroyo et al, 2022; Meynet et al, 2020). However, such limitations (i.e.…”

“…It is applied under the assumption of a single, rate‐limiting, thermally activated process and an activation energy independent of temperature. This approach is widely used to temperature correct (bio‐)chemical processes such as degradation and membrane passage (EFSA, 2008; Filippov et al, 2003; Meynet et al, 2020) but is also used to describe physiological processes such as oxygen consumption (standard metabolic rate) (Arroyo et al, 2022; Brown et al, 2004). Furthermore, recent studies successfully applied the Arrhenius equation to evaluate thermal stress (Jørgensen et al, 2021) or temperature correct toxicity data (Gergs et al, 2019) of different species test systems.…”

Variation in thermal physiology can drive the temperature-dependence of microbial community richness

“…Our theory predicts that the decline of species richness above intermediate temperatures occurs because variation in growth rate across populations reduces the number of species that are able to persist under competitive pressure, as the environment becomes hotter. This contrasts with other explanations of unimodality in thermal responses such as enzyme kinetics (Kontopoulos et al, 2018;Arroyo et al, 2022) or the metabolic niche hypothesis (Clarke and Gaston, 2006) which invoke reduced metabolic rates at high temperatures (above the OTR), either because of the inactivation of enzymes or the reduction in number of viable metabolic strategies, to explain the decline in coexisting species. Furthermore, the observed patterns of negative covariance seen in existing data (analysed here; (Smith et al, 2019(Smith et al, , 2021) suggest that peaks of richness should occur towards the higher end of the operational temperature ranges (OTRs) of most mesophilic bacteria, a prediction that is consistent with unimodal microbial species temperature-richness relationships observed in the real world (Milici et al, 2016;Sharp et al, 2014;Thompson et al, 2017).…”

“…The copyright holder for this preprint this version posted October 28, 2022. ; https://doi.org/10. 1101/2022.10.28.514215 doi: bioRxiv preprint et al, 2014Thompson et al, 2017). Indeed, as demonstrated by the data-synthesis by Hendershot et al (2017), the temperature responses of microbial richness or diversity are "consistently inconsistent", with no single pattern in terms of shape (monotonic or unimodal) or direction (positive or negative) dominating.…”

A general theory for temperature dependence in biology

“…All data and R codes used in the preparation of figures and in statistical analyses can be found in GitHub ( 67 ).…”