Cruz-Loya Mauricio, Mordecai Erin A, Savage Van M
Department of Biology, Stanford University.
Department of Computational Medicine, University of California, Los Angeles.
bioRxiv. 2024 Aug 6:2024.08.01.605695. doi: 10.1101/2024.08.01.605695.
Temperature responses of many biological traits-including population growth, survival, and development-are described by thermal performance curves (TPCs) with phenomenological models like the Briere function or mechanistic models related to chemical kinetics. Existing TPC models are either simple but inflexible in shape, or flexible yet difficult to interpret in biological terms. Here we present flexTPC: a model that is parameterized exclusively in terms of biologically interpretable quantities, including the thermal minimum, optimum, and maximum, and the maximum trait value. FlexTPC can describe unimodal temperature responses of any skewness and thermal breadth, enabling direct comparisons across populations, traits, or taxa with a single model. We apply flexTPC to various microbial and entomological datasets, compare results with the Briere model, and find that flexTPC often has better predictive performance. The interpretability of flexTPC makes it ideal for modeling how thermal responses change with ecological stressors or evolve over time.
许多生物学特性(包括种群增长、存活和发育)的温度响应是通过热性能曲线(TPCs)来描述的,这些曲线使用诸如Briere函数之类的现象学模型或与化学动力学相关的机理模型。现有的TPC模型要么形状简单但缺乏灵活性,要么灵活却难以从生物学角度进行解释。在此,我们提出了flexTPC模型:一种仅根据生物学可解释的量进行参数化的模型,这些量包括最低温度、最适温度、最高温度以及最大性状值。FlexTPC能够描述任何偏度和热广度的单峰温度响应,从而可以使用单一模型对不同种群、性状或分类群进行直接比较。我们将flexTPC应用于各种微生物和昆虫学数据集,与Briere模型比较结果,发现flexTPC通常具有更好的预测性能。FlexTPC的可解释性使其成为模拟热响应如何随生态压力因素变化或随时间演变的理想模型。