Body temperature evolves strikingly slowly over macroevolutionary timescales in land vertebrates (tetrapods), yet the mechanism enforcing this slowness is unresolved. Waldron et al. (2025) proposed a bottom-up explanation: because organismal body temperature must accommodate the thermal performance curves of many simultaneously evolving proteins, adaptive shifts in body temperature are constrained by the slow, coordinated re-optimization of the proteome — a molecular instance of the cost of complexity. They supported this hypothesis in silico, with individual-based simulations of QTL-encoded protein traits together with analytical approximations, while stating that their goal was not to fit or precisely replicate empirical rates of evolution. Whether such molecular-scale constraints can quantitatively explain the observed phylogenetic rates of body-temperature evolution across endothermic and ectothermic tetrapods remains empirically untested. The proposing work frames the mechanism as an alternative — explicitly not mutually exclusive — hypothesis to long-term stabilizing selection; behavioral thermoregulation (the Bogert effect) and low heritability of thermal traits are further explanations discussed in the literature. A resolution requires coupling proteome-scale thermal-performance data with population-genetic modeling and phylogenetic rate estimates.

Background

Body temperature (TbT_b) is among the most consequential physiological variables of vertebrates: it sets the operating temperature of nearly every biochemical reaction in the body. Land vertebrates (tetrapods) divide broadly into endotherms (birds and mammals, which metabolically maintain a high, fairly constant TbT_b) and ectotherms (reptiles and amphibians, whose TbT_b largely reflects environmental and behaviorally managed heat exchange). Whether thermal physiology is evolutionarily rigid or labile has been debated for decades, reviewed early on by Angilletta, Niewiarowski and Navas (2002). Phylogenetic comparative methods — statistical analyses that fit stochastic models such as Brownian motion or Ornstein–Uhlenbeck processes to trait values on evolutionary trees in order to estimate rates and modes of evolution, including bounded-evolution models in which traits are confined between hard limits (Boucher and Démery 2016) — consistently find that TbT_b evolves very slowly: Moreira, Qu and Wiens (2021) documented strong phylogenetic signal and conservatism of TbT_b over roughly 350 million years of tetrapod history, with lower rates in endotherms than ectotherms, and the companion analysis of Tarimo, White, Bodensteiner, Muñoz, Waldron and Uyeda (2025) reports shared limits on the rate of thermal-physiology evolution across endothermic and ectothermic clades. This slowness echoes the classic paradox of stasis — abundant short-term variation and selection yet persistent long-term stasis — for which Estes and Arnold (2007) developed the stabilizing-selection account, and it parallels the finding that past rates of climatic-niche evolution are far slower than projected climate change (Quintero and Wiens 2013). Dominant explanations are extrinsic or behavioral: long-term stabilizing selection, the Bogert effect (behavioral thermoregulation buffers body temperature from directional selection, so thermal physiology evolves inertia; Huey, Hertz and Sinervo 2003; Muñoz and Bodensteiner 2019), and low heritability of thermal traits despite genetic divergence (Logan et al. 2018).

A third, mechanistically bottom-up possibility was articulated by Waldron, Tarimo, Sashittal, Naughton, Muñoz and Uyeda (2025). Each protein has a thermal performance curve — a typically bell-shaped function describing how its activity or stability depends on temperature, with an optimum near the species' TbT_b; cell-wide measurements show that proteins unfold across a limited, marginally stable temperature range (Leuenberger et al. 2017), and adaptive shifts of protein thermal optima require specific sequence changes whose effects are partly contingent on other residues (Dong et al. 2018; Liao, Somero and Dong 2018). Because organismal TbT_b is a single organism-level trait that must simultaneously accommodate thousands of such curves, TbT_b can change only as fast as the proteome can be coordinately re-optimized. This is a molecular instance of the cost of complexity (Orr 2000): the more functionally independent components a trait depends on, the slower its adaptive response, because mutations that would move one component's optimum tend to disrupt others. Waldron et al. (2025) tested this logic in silico using the SLiM 4 individual-based genetic simulation framework (Haller and Messer 2023), modeling protein performance curves and organismal TbT_b as quantitative-trait-locus-encoded characters (QTL: genome regions statistically associated with trait variation) whose combined effects determine fitness. More constrained proteins drastically slowed simulated TbT_b evolution, and simulated environmental shifts produced a rapid initial pulse followed by long, sluggish phases — qualitatively matching the coexistence of microevolutionary lability and macroevolutionary stasis. However, the authors state that the goal of their simulations "was not to fit or precisely replicate empirical rates of evolution, which would require more precise empirical estimates of mutational effects and connections between performance and fitness," and they frame the mechanism as a hypothesis that "might contribute" to observed stasis; whether it can quantitatively explain the empirically estimated rates of TbT_b evolution remains untested.

Problem Statement

Determine whether molecular-scale constraints arising from protein thermal evolution — the requirement that organismal body temperature remain compatible with the thermal performance curves (bell-shaped functions describing how each protein's activity or stability depends on temperature) of the many proteins an animal expresses — can quantitatively explain the slow macroevolutionary rates of body temperature observed across land vertebrates (tetrapods), as proposed by Waldron et al. (2025).

Resolving the question requires (i) grounding the mechanism in empirically measured or independently inferable quantities — the number of thermally constrained proteins, the distribution of their thermal optima and performance-curve widths, the effects of mutations on protein thermal performance, and the relevant population-genetic parameters; (ii) deriving or simulating the body-temperature evolutionary dynamics these constraints imply; and (iii) quantitatively comparing the predicted rates and patterns with phylogenetically estimated macroevolutionary rates of body temperature across tetrapod clades — including the lower rates in endotherms than ectotherms and the shared limits reported across clades.

A quantitative confirmation or a demonstrated quantitative failure of the mechanism (under realistic parameters) would each resolve the question; simulation-only plausibility arguments leave it open, because the proposing work itself states that its simulations were not intended to fit empirical rates, and its hypothesis remains to be tested against data.

References

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