No model to rule them all: a systematic comparison of 83 thermal performance curve models across traits and taxonomic groups

Abstract

In ectotherms, the performance of physiological, ecological, and life-history traits universally increases with temperature to a maximum before decreasing again. Identifying the most appropriate thermal performance model for a specific trait type has broad applications, from metabolic modelling at the cellular level to forecasting the effects of climate change on population, ecosystem, and disease transmission dynamics. To date, numerous mathematical models have been designed, but a thorough comparison among them is lacking. In particular, we do not know if certain models consistently outperform others, and how factors such as sampling resolution and trait or organismal identity influence model performance. These have led researchers to select models semi-arbitrarily, potentially introducing biases that compromise environmental suitability predictions. To fill these gaps, we collect 2,739 thermal performance datasets from diverse traits and taxa, to which we fit 83 models used in previous studies. We detect remarkable variation in model performance which is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results highlight a lack of well-defined scenarios in which certain models are more appropriate than others. Therefore, to avoid likely model-specific biases, future studies should perform model selection to identify the most appropriate model(s) for their data.

Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature

“…Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature Sean T. Michaletz 1,2 | Josef C. Garen 1,2 kinetics using six or seven parameters (Kontopoulos et al, 2023). This paper reviews the development and formalization of prominent hypotheses for the temperaturedependence of biological rates.…”

“…Several hypotheses have been advanced to explain biological temperature responses, including temperature‐dependent changes in collision frequency and conformation of biochemical reactants and enzymes (Arcus et al., 2016; Arrhenius, 1907; Arroyo et al., 2022; DeLong et al., 2017; Evans & Polanyi, 1935; Eyring, 1933; Johnson et al., 1942), and product inhibition resulting from different temperature sensitivities of product formation and diffusion (Ritchie, 2018). These hypotheses have been formalized in numerous models that vary in level of mechanism and mathematical complexity (Angilletta Jr., 2006; DeLong et al., 2017; Kontopoulos et al., 2023; Padfield et al., 2021). For example, models range from those that offer useful phenomenological descriptions of asymmetric responses using only three parameters (Briere et al., 1999; Mordecai et al., 2013, 2017, 2019), to those that mechanistically characterize biochemical kinetics using six or seven parameters (Kontopoulos et al., 2023).…”

“…These hypotheses have been formalized in numerous models that vary in level of mechanism and mathematical complexity (Angilletta Jr., 2006; DeLong et al., 2017; Kontopoulos et al., 2023; Padfield et al., 2021). For example, models range from those that offer useful phenomenological descriptions of asymmetric responses using only three parameters (Briere et al., 1999; Mordecai et al., 2013, 2017, 2019), to those that mechanistically characterize biochemical kinetics using six or seven parameters (Kontopoulos et al., 2023).…”

“…Numerous models have been introduced to address the challenges of using the monotonic Arrhenius model to characterize unimodal temperature response data (Angilletta Jr., 2006; Kontopoulos et al., 2023; Padfield et al., 2021). Here we mention only a small set of these models that extend the Arrhenius model in various ways.…”

“…The model proposes that both unimodal temperature responses and the enzyme inactivation previously hypothesized to drive these responses (Johnson & Lewin, 1946; Schoolfield et al., 1981) originate from underlying changes in conformational entropy. Second, with only three parameters, the GTD model is among the simplest unimodal models available (Kontopoulos et al., 2023; Padfield et al., 2021). Importantly, these parameters have explicit thermodynamic meaning and can be independently estimated.…”