Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, UK.
LOEWE Centre for Translational Biodiversity Genomics, Frankfurt, Germany.
Nat Commun. 2024 Oct 14;15(1):8855. doi: 10.1038/s41467-024-53046-2.
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. To fill this knowledge gap, we compile 2,739 thermal performance datasets from diverse traits and taxa, to which we fit a comprehensive set of 83 existing mathematical models. We detect remarkable variation in model performance that is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results reveal a surprising lack of well-defined scenarios in which certain models are more appropriate than others. To aid researchers in selecting the appropriate set of models for any given dataset or research objective, we derive a classification of the 83 models based on the average similarity of their fits.
在变温动物中，生理、生态和生活史特征的表现普遍随着温度的升高而增加，达到最大值后再次下降。为特定特征类型识别最合适的热性能模型具有广泛的应用，从细胞水平的代谢建模到预测气候变化对种群、生态系统和疾病传播动态的影响。迄今为止，已经设计了许多数学模型，但它们之间缺乏彻底的比较。特别是，我们不知道某些模型是否始终优于其他模型，以及采样分辨率、特征或生物个体身份等因素如何影响模型性能。为了填补这一知识空白，我们从不同的特征和分类群中编译了 2739 个热性能数据集，并为其拟合了一套全面的 83 个现有数学模型。我们发现模型性能存在显著差异，而这种差异并不是主要由采样分辨率、特征类型或分类信息驱动的。我们的结果表明，在某些模型比其他模型更合适的情况下，并没有明确的定义。为了帮助研究人员为任何给定的数据集或研究目标选择合适的模型集，我们根据拟合的平均相似度对 83 个模型进行了分类。
