A flexible model for thermal performance curves.

Area of Science:

• Ecology
• Evolutionary Biology
• Physiology

Background:

• Biological traits like growth and survival are often temperature-dependent, described by thermal performance curves (TPCs).
• Existing TPC models are either too simple and inflexible or too complex and biologically uninterpretable.
• Phenomenological models (e.g., Briere function) and mechanistic models (e.g., chemical kinetics) have limitations in flexibility and interpretability.

Purpose of the Study:

• To introduce flexTPC, a novel model for thermal performance curves.
• To develop a TPC model parameterized with biologically meaningful quantities (thermal minimum, optimum, maximum, and maximum trait value).
• To create a flexible model capable of describing TPCs of varying skewness and thermal breadth for direct comparisons.

Main Methods:

• Developed the flexTPC model, which uses biologically interpretable parameters.
• Applied flexTPC to microbial and entomological datasets.
• Compared the predictive performance of flexTPC against the established Briere model.

Main Results:

• FlexTPC successfully describes unimodal temperature responses with diverse shapes and thermal breadths.
• The model demonstrates superior predictive performance compared to the Briere model across various datasets.
• Parameterization in terms of biological optima and limits enhances model interpretability.

Conclusions:

• FlexTPC offers a flexible and biologically interpretable framework for modeling thermal performance curves.
• The model facilitates direct comparisons of thermal responses across different populations, traits, or taxa.
• FlexTPC is well-suited for studying how thermal responses are affected by ecological stressors or evolve over time.