📄 evolutionary biology

A general model for the evolution of thermal performance curves with application to real time-series data

This paper presents a flexible mathematical and simulation model that integrates genetic architecture and physiological constraints to predict the evolutionary trajectories of thermal performance curves, successfully explaining complex dynamics like multi-generation lags and individual variability observed in real-world data such as that of the invasive pest *Drosophila suzukii*.

Original authors: Min, J., Chapman, Z., McCabe, E., Nunez, J. C. B., Teets, N., Lotterhos, K. E.

Published 2026-06-20

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CC BY 4.0

Original authors: Min, J., Chapman, Z., McCabe, E., Nunez, J. C. B., Teets, N., Lotterhos, K. E.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine every living creature has a built-in "performance engine" that works best at a specific temperature. If it's too cold, the engine sputters; if it's too hot, it overheats. Scientists call this relationship a Thermal Performance Curve (TPC). It's like a graph showing how fast a bug can run, how well a plant grows, or how efficiently a fish swims at different temperatures.

For a long time, scientists have known these curves exist, but they've been missing a rulebook for how these curves change over time as the weather shifts. They didn't have a good way to predict how an animal's "engine" would evolve if the climate started acting up, especially considering the messy reality of genetics and how bodies actually work.

This paper builds a new digital simulator to fill that gap. Think of it as a high-tech video game where the scientists can play out thousands of years of evolution in a computer. They programmed this game to include:

Real weather patterns: Not just a steady temperature, but days that are hot, cold, and unpredictable.
Family trees: How traits are passed down from parents to offspring.
Body limits: The physical rules that stop an animal from becoming a perfect heat-proof machine overnight.

What did they discover?

The "Jack-of-All-Trades" Effect: In places where the weather is unpredictable (like a rollercoaster of temperatures), the simulator shows that animals tend to evolve into "generalists." They don't become super-fast at one specific temperature; instead, they become "okay" at a wide range of temperatures, just like a Swiss Army knife is useful for many tasks but perfect at none.
The Lag Effect (The "Echo"): This is the most surprising finding. The paper shows that an animal's performance curve doesn't change instantly when the temperature changes. There is a multi-generation delay, like an echo in a canyon. The animal's "engine" might still be tuned for the weather of three generations ago, even if the current weather is totally different. This can create confusing patterns where the animal seems to be doing well in temperatures that don't match its current environment.
Real-World Proof: The scientists tested their simulator against a real-life invader, the spotted-wing drosophila (Drosophila suzukii). Their model successfully predicted exactly how this pest's cold tolerance has been changing, including the individual differences between bugs and the time-lag in their adaptation.

Why does this matter?

The paper warns that if you try to predict how species will survive climate change using simple models that ignore things like family history, population size, or the fact that weather often comes in streaks (hot days followed by more hot days), you will get the wrong answer.

By using this flexible, detailed simulator, scientists can now generate specific, testable guesses about how different species will evolve as the planet warms, rather than just guessing based on simplified assumptions. It's a tool to help us understand the complex, slow-motion dance between life and a changing climate.

Technical Summary: A General Model for the Evolution of Thermal Performance Curves

Problem Statement
Thermal performance curves (TPCs) are fundamental tools for understanding how body temperature changes affect organismal performance. While empirical evidence confirms that temperature-dependent performance is ubiquitous across taxa, the field currently lacks robust models capable of describing how TPCs evolve under realistic conditions. Specifically, existing frameworks often fail to account for the complexities of realistic time-series data, diverse genetic architectures, and physiological constraints. This gap limits the ability to predict evolutionary trajectories and the resulting shapes of TPCs in response to fluctuating thermal regimes.

Methodology
To address these limitations, the authors integrate a mathematical model with individual-based quantitative population genetic simulations. This hybrid approach allows for the simulation of evolutionary dynamics under controlled, yet realistic, environmental and genetic parameters. The model is designed to predict the evolutionary trajectory and shape of TPCs for any specified thermal regime. Crucially, the simulation framework incorporates several factors often omitted in simpler models, including:

Autocorrelation in temperature time-series.
Effective population size ( $N_e$ ).
The evolution of additive genetic correlations between TPC parameters.
Specific genetic architectures.
Physiological constraints.

Key Contributions and Results
The study yields several critical insights into the dynamics of thermal adaptation:

Reproduction and Extension of Known Dynamics: The model successfully reproduces core properties of TPC evolution established in previous literature, such as the emergence of generalist phenotypes in variable environments.
Mismatches in Thermal History: The model explains a counter-intuitive phenomenon where organisms evolve TPCs that do not align with their historical body temperature ranges, a pattern often observed in nature but difficult to explain with static models.
Novel Adaptive Tracking Dynamics: A primary finding is the identification of "multi-generation lags" between environmental temperature and TPC parameters. These lags can generate unexpected correlations between temperature and performance traits, complicating the interpretation of adaptive tracking.
Empirical Validation: The model was applied to real time-series data from the invasive pest Drosophila suzukii. It successfully predicted empirically observed patterns regarding the adaptive tracking of the critical thermal minimum. Notably, the model captured both individual-level variability and the multi-generation lags associated with changing temperatures, demonstrating its capacity to mirror complex biological realities.

Significance and Claims
The paper positions its work as a necessary advancement in evolutionary ecology by highlighting the limitations of models that ignore critical factors such as time-series autocorrelation, genetic architecture, and individual variability. By providing a flexible simulation model that incorporates these elements, the authors claim to offer a tool that can generate empirically testable hypotheses regarding how species will evolve in response to global climate change. The significance lies not in predicting specific future outcomes for all species, but in providing a rigorous framework to understand the constraints and dynamics that govern TPC evolution under realistic, fluctuating thermal regimes.

Technical Summary: A General Model for the Evolution of Thermal Performance Curves

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