Warming is expected to increase microbial respiration rates (Smith et al. 2019) due to the fundamental relationship be-tween physiology and environmental temperature (Gillooly et al. 2001;Brown et al. 2004). Adaptation to higher temperatures through evolution may offset this increase, but the degree to which organisms can overcome basic thermodynamic constraints through evolution is unclear (Visser 2008;Angilletta et al. 2010;Hoffman and Sgrò 2011). If a universal temperature dependence is assumed, as in the metabolic theory of ecology (Brown et al. 2004), it is expected that biological rates in warm-adapted microbes, including respiration, should still be higher than those in cold-adapted species (Smith et al. 2019). The alternative-if respiration rates remain the same regardless of what temperature the population evolved at (e.g., Kutcherov 2016)would indicate that evolution can fully compensate for temperature dependence.
Part of the challenge in resolving these alternate hypotheses is the complexity of the temperature-respiration relationship. Fundamentally, biological rates are constrained by thermodynamics (Huey and Kingsolver 1989;Brown et al. 2004): higher temperatures increase the activity of enzymes, resulting in faster reaction rates. However, physiological factors at the cellular level add additional constraints on top of these thermodynamic effects. For example, stress is endured at low temperatures (e.g., from protein misfolding; Guisbert et al. 2008) and high temperatures (e.g., from reduced membrane fluidity; Beney and Gervais 2001), with increased respiration rates driven by the costs of acclimation to unfavorable environmental temperatures (Hall et al. 2010). As a result, microbes typically have a thermal optimum that reflects the environmental temperature at which they have evolved (Thomas et al. 2012).
When organisms are exposed to new temperatures, changes in their respiration rates may therefore arise from at least three factors: (1) underlying thermodynamic constraints, (2) phenotypic plasticity, and (3) evolutionary change (Somero 2010;Schulte et al. 2011;Marasco et al. 2023). Phenotypic plasticity is physiological variability without genetic change (Fox et al. 2019), such as shifts in an organism's metabolic strategy by altering the balance between different metabolic processes within the cell (e.g., Wilken et al. 2013;Cabrerizo et al. 2019). In contrast, evolutionary adaptation entails genetic modification of a population through selection that specifically increases an organism's fitness. For example, evolutionary adaptation may ameliorate stress-induced respiratory costs when organisms are exposed to new temperatures, or it may modulate underlying thermodynamic reaction rates-or perhaps it may do both to some degree. To avoid confusion, here we will use "evolved" to characterize individuals that have undergone selection at a given temperature, resulting in, for example, new enzyme variants with different thermal tolerances. And we will use "acclimated" to characterize individuals that are briefly exposed to a temperature different to the environment in which they evolved, inducing a plastic physiological response, such as a change in metabolic strategy.
In a variable temperature environment, evolutionary and plastic changes interact across different timescales. The thermal response of metabolic traits mediated by enzyme kinetics operates on physiological timescales, often assumed to be instantaneous in ecological contexts (Huey and Kingsolver 1989). The plastic response associated with acclimation requires a reallocation of resources to alter metabolic activity and can occur over hours or days (Hofmann and Todgham 2010;Calbet and Saiz 2022). In contrast, evolution requires many generations for accumulated mutations to spread throughout the population and occurs on a timescale related to the generation time of the organism. Plasticity itself can also evolve, with organisms becoming more or less plastic under selection from environmental variability (Scheiner 1993;Archibald et al. 2024). Inconsistencies in the definition and use of different timescales (and, relatedly, scales of organization across individuals, populations, and species; Phillips et al. 2014) may account in some part for seemingly conflicting results in how adaptive mechanisms modulate the thermal response of metabolic traits, including respiration, in microbes (Yvon-Durocher et al. 2012).
Mixotrophic microbes are single-celled organisms that combine different nutritional modes within the same cell. Mixotrophs represent a compelling case study of the thermal evolution of respiration. Mixotrophs integrate both autotrophic and heterotrophic metabolic processes within the same cell (Stoecker et al. 2017) and play an important role in global biochemical cycles (Mitra et al. 2014;Edwards 2019;Ward 2019). Mixotrophs often display high levels of metabolic plasticity as well, capable of responding to shifting resource requirements in variable environments by altering their relative investments into different metabolic processes (Lie et al. 2018;Wilken et al. 2020). These unique metabolic characteristics, combined with the generally high evolutionary potential of microorganisms, are likely to make the interaction between plastic and evolutionary adaptive mechanisms in mixotrophs particularly pronounced. Lepori-Bui et al. (2022) reported evidence of adaptive evolution in marine mixotrophic protists of the genus Ochromonas exposed to warming. They found that in addition to evolving faster growth rates, mixotrophs that had evolved at high temperature had lower rates of respiration than those only briefly acclimated to high temperature, demonstrating that evolution can compensate for thermal sensitivity to some degree. Here, we develop a model of mixotroph metabolism that includes both evolution and plasticity to explore the mechanisms underlying thermal adaptation and its effects on carbon budgets. We propose that (1) evolution in mixotrophs partially compensates for thermal sensitivity in respiration by minimizing the physiological costs associated with acclimation; (2) the differences in respiration between evolved and acclimated mixotrophs affect their carbon budget, thereby driving variability in metabolic strategy; and (3) evolution and plasticity interact across different timescales to produce transient thermal responses in metabolic activity following a temperature change. The behavior of this model, compared against experimental data from Lepori-Bui et al. ( 2022), establishes a framework for evaluating the eco-evolutionary dynamics driving variability in microbial metabolic activity under warming temperatures.
We developed a mechanistic model of mixotroph growth and respiration based on a set of differential equations that describe short-term plastic changes to metabolic strategy and long-term evolutionary changes to respiration in response to temperature variability (fig. 1). The model is a modified version of a previously published mixotroph model (Archibald et al. 2024), here adapted to represent the experimental setup used in Lepori-Bui et al. (2022). In addition to modifying the resource landscape to reflect the specific biology of Ochromonas, a temperature-dependent respiration function was added that includes both the environmental temperature and the temperature at which the mixotroph population evolved. We summarize the main components of the model here; for a full description of the model and the differences from the version published by Archibald et al. (2024), see "Model Description" in the supplemental PDF. The model describes a population of mixotrophs (M) that grows utilizing carbon and nitrogen acquired through a combination of photosynthesis (P) and grazing (G) on bacteria. The specific metabolic strategy of the population is represented via relative investments into growth (r u ), photosynthesis (r p ), and grazing (r g ). Changes to these investment parameters represent plastic responses to environmental variability reflecting the mixotrophs' shifting resource requirements. The metabolic strategy is not assigned ad hoc but is allowed to arise as a function of environmental conditions (temperature, light, prey abundance) by choosing the set of r that maximizes growth rate, U (for details, see "Model Description" in the supplemental PDF). The terms U, P, and G depend on temperature fol-lowing power-scaling laws that represent the thermodynamic effects of temperature on reaction kinetics (Brown et al. 2004; fig. 1B, 1C, pink lines). Mixotroph population growth is balanced by losses through temperaturedependent respiration (R),
Two terms comprise R(T): one to describe the thermodynamic effects of temperature (fig. 1D, pink line), and an additional term that describes acclimation costs (fig. 1D, yellow line) relative to the temperature the population evolved at (T evo ). Acclimation costs are zero when T p T evo and increase as the difference between T and T evo increases: Photosynthesis supplies carbon to the mixotroph, and grazing on bacteria supplies both carbon and nitrogen. Metabolic plasticity is represented as dynamic relative investments (r) into photosynthesis, grazing, and growth to maximize growth rate. Biomass is lost through respiration. B, C, Photosynthetic (B) and grazing (C) rates change with temperature through fundamental thermodynamic scaling (pink) combined with variation in plastic investments across temperature gradients. D, Respiration rate also has a baseline thermal scaling (pink) and additional acclimation costs (yellow) associated with poor performance when the mixotroph is grown at temperatures away from the temperature it was evolved at (T evo ). E, Net growth rate is the difference between biomass gains driven by photosynthesis, grazing, and growth investments and biomass losses through respiration. Note that the temperature where growth is maximized is slightly higher than the temperature where respiratory losses are minimized (i.e., T evo ).
where r 0 is the basal respiration rate at reference temperature T 0 , Q 10,R is the thermodynamic scaling of respiration, and r a is the acclimation costs of respiration. For simplicity, we assume that the plastic potential of the population and the acclimation costs of respiration are fixed (see table S1, available online) and do not evolve through exposure to different temperature conditions. The net growth rate of the population (dM=dt) follows a unimodal function of temperature (fig. 1E). The maximum of this function occurs at a temperature slightly above T evo due to the relative values of the derivatives of several temperature-dependent terms (i.e., the increase in growth rate driven by thermal scaling of photosynthesis and grazing outpaces the increase in respiration at T p T evo ). A similar difference between mean environmental temperature and the optimal temperature for growth has been documented on a global scale in phytoplankton, both empirically and with eco-evolutionary modeling (Thomas et al. 2012).
Evolution in response to thermal variability is represented by changes to the mixotroph's evolutionary temperature. The variable T evo is dynamic and converges toward the environmental temperature, following
where l is the rate of evolution. The choice of l defines the timescale of the evolutionary response. Lepori-Bui et al.
(2022) observed evidence of evolution within 50 generations (about 100-200 days). Here, we choose l such that T evo asymptotically converges to T over similar timescales.
In contrast, variability in the metabolic investment strategy (r), which characterizes the plastic response to temperature, occurs on a timescale of about one day (Calbet and Saiz 2022).
The model was simulated across a range of temperatures (167C-327C) to explore the differences in metabolic behavior of mixotrophs in their evolved and acclimated states. We designed a set of simulations (described below) to mimic the experimental design of Lepori-Bui et al. (2022). Comparisons between the observed and simulated results provide a framework to evaluate the mechanisms represented in the model. We explore the interaction of timescales between plastic and evolutionary responses to temperature by simulating long-term time series of mixotroph evolution following an instantaneous temperature change.
We first summarize the empirical results of Lepori-Bui et al. (2022) in order to contextualize our model simulations.
The authors evolved Ochromonas populations at 187C, 247C, and 307C for 3 years and conducted reciprocal transplant assays between the evolved strains at each temperature. When assayed at a nonevolutionary temperature, the mixotrophs were acclimated for a period of 5 days in order to allow plastic changes to metabolic activity to occur. Metabolic traits were measured at the end of this acclimation period and compared between mixotrophs that had evolved at 187C and 307C and controls that had evolved at 247C and then acclimated to high and low temperatures. Lepori-Bui et al. found that mixotrophs evolved at 187C and 307C had lower rates of respiration compared with mixotrophs evolved at 247C and then acclimated to those temperatures for 5 days (fig. 2A). Evolved mixotrophs also had higher carbon use efficiency (fig. 2C), increased photosynthetic rates (fig. 2E), decreased grazing rates (fig. 2G), and higher net growth rates (fig. 2I) than mixotrophs that were only acclimated. These metabolic differences provided evidence of evolutionary adaptation over the course of the 3-year study. The data also suggest a link between carbon budget and evolved fitness, although the mechanism of the evolved fitness increase was not clear from the empirical data alone.
Simulations with our model provide additional insight into the observed evolutionary changes in metabolic activity and a possible mechanism for the differences in carbon budget. Figure 3 shows two such sets of simulation experiments. In the first set of experiments (fig. 3, left), the model was simulated for 50 days (approximately 25 generations) at 247C to allow the model to reach a pseudo equilibrium (constant growth rate and metabolic investments) and minimize the effects of initial conditions. The temperature was then increased to 307C for an additional 150 days (approximately 75 generations). Two mixotroph populations were simulated under these conditions: a control population with l p 0 (no evolution) and an evolving population with l p 0:02, such that T evo asymptotically increases toward the simulation temperature. In another set of experiments (fig. 3, right), the same model populations were simulated under a temperature decrease from 247C to 187C, again with simulations for both evolving and nonevolving populations.
Following a transient period of about 10 days after model initialization, both the control population and the evolving population established a stable metabolic strategy to maximize their growth rate at 247C (fig. 3). Immediately following the temperature change, both populations experienced steep declines in growth rate (fig. 3E, 3F) driven by the increased respiration rates (fig. 3G, 3H) associated with the acclimation costs of growing at a temperature different from what they evolved at. Within several days, plastic changes to the relative investments into photosynthesis (fig. 3I, 3J) and grazing (fig. 3K, 3L) help accommodate E 18 24 30 0 0.5 1 1.5 F 18 24 30 0 0.1 0.2 0.3 0.4 Grazing rate (d -1 ) G 18 24 30 0 0.1 0.2 0.3 0.4 H 18 24 30 0 0.2 0.4 0.6 Net growth rate (d -1 ) I 18 24 30 0 0.2 0.4 0.6 J Figure 2: Thermal responses in metabolic activity compared across empirical data from Lepori-Bui et al. (2022; left column) and model simulations (right column).
The plastic response of mixotrophs with T evo p 247C is shown in gray, while the evolved response of mixotrophs adapted to their thermal environment (T evo p T) is shown in black (blue and red points represent the low and high experimental temperatures, respectively). Experimental data were measured only at the points shown, but the model was simulated at all points along the solid lines (points shown for reference). For the experimental data, vertical lines represent the standard error interval over six replicates.
for the altered carbon budget. Specifically, mixotrophs acclimated plastically to altered temperatures by increasing photosynthesis and decreasing grazing; because photosynthesis is a more efficient source of carbon, this increases the C∶N ratio of resource acquisition in response to increased carbon demand driven by higher respiration rates.
These plastic investment changes are accompanied by an increase in growth rate as the mixotroph adapts its metabolic strategy to the new temperature conditions. For the evolving population (l p 0:02) over the next 150 days, T evo converges toward the altered environmental temperature (fig. 3A I 0 50 100 150 200 0.5 1 Grazing investment (ρ g ) K 0 50 100 150 200 18 24 30 B 0 50 100 150 200 0 5 10 15 D 0 50 100 150 200 -0.5 0 0.5 F 0 50 100 150 200 0 0.5 1 1.5 H 0 50 100 150 200 0 0.5 J 0 50 100 150 200 0.5 1 L Figure 3: Time series of model simulation showing short-and long-term responses in metabolic activity in mixotrophs following an instantaneous temperature change (increase [left column] or decrease [right column]).
Immediately following the temperature change, respiration rates increase. In the short term, mixotrophs respond by increasing (decreasing) investment into photosynthesis (grazing) to meet their higher carbon needs. In the long term, evolution moves T evo to match the environmental temperature, resulting in declining respiration rates and a gradual return to baseline metabolic traits that are more similar to the pre-evolved state. The colored circles indicate points on the time series that are analogous to the conditions plotted in figure 2. Small perturbations to growth rate and metabolic activity were observed associated with simulated periodic dilutions (for details, see the supplemental PDF).
individuals better suited for higher temperatures. As the difference between T and T evo decreases, the corresponding acclimation cost component of the respiration function (eq. [2]) also decreases, resulting in lower respiration rates in the evolved population (fig. 3). The induced plastic changes to metabolic strategy revert toward the ancestral state over the same time period to reflect the evolved reductions in respiration.
The model broadly reproduces the observed differences in metabolic traits between evolved and acclimated mixotrophs (fig. 2). The model suggests that increased respiration rates in acclimated mixotrophs are driven by the biological costs of being poorly adapted to their thermal environment rather than the direct thermodynamic effect of temperature on respiration rate. Furthermore, the model suggests that the differences in photosynthesis and grazing observed between evolved and acclimated mixotrophs are driven by an increase in the carbon demand associated with the increased respiration rates. Evolution reduces respiration by minimizing the acclimation costs. This means that the differences in growth rate and metabolic activity between mixotrophs evolved at different temperatures is smaller than the differences between mixotrophs evolved at the same temperature and acclimated to different temperatures. These results suggest that evolution acts as a powerful stabilizing force over evolutionary timescales. Given enough time and a sufficiently small cost of adaptation, evolution will offset a portion of the temperature-driven variability in mixotroph metabolism and help stabilize the metabolic strategy of mixotrophs across a broad range of temperatures.
"Given enough time" is a key qualifier to this statement. While the differences between the starting and ending points of the evolving population in the model simulations are small, there is significant transient activity that occurs in the interim (fig. 3). Short-term plastic changes in response to temperature changes happen on a timescale of several days, while the restoring force of evolution plays out over hundreds of days. It is also possible that plasticity itself evolved over the course of the experiment, with a fixed temperature (i.e., low variability) environment creating selective pressure for reduced plastic potential (although the relationship between environmental fluctuation and adaptive plasticity remains an active area of research; King and Hadfield 2019;Archibald et al. 2024).
The interaction across timescales results in large, transient thermal responses that likely have important ecosystem implications and may be overlooked if we focus solely on long-term differences between evolved populations. Similar transient responses to warming have been observed in the respiration rates of soil microbes. Acclimation to warming has been shown to reduce thermal sensitivity on decadal timescales, weakening the positive feedbacks be-tween microbial respiration and the carbon cycle (Luo et al. 2001). In some cases, metabolic adaptation resulted in a reversal of long-term warming trends and a return to the prewarming ecosystem state (Oechel et al. 2000). Our model results indicate that similar dynamics may play out in marine ecosystems; microbial evolution is a powerful stabilizing force for ecosystem function across diverse biomes, reducing thermal sensitivity and offsetting or reversing the short-term effects of warming.
While our model reproduced most of the patterns found in the empirical data, the model and data diverged in their predictions of changes in grazing (fig. 2G, 2H), with the model predicting increases with evolution and the empirical data indicating decreases. Higher temperatures may trigger the mixotroph to increase their investment into some cellular process not represented in the model, resulting in a decrease in resources available for investment into grazing. Alternatively, the decrease in grazing rate could reflect a change in the efficiency in grazing arising from an unidentified change in bacterial availability in the experimental setup. Nevertheless, the remaining flux terms (photosynthesis, respiration, and net growth rate) agree quite well across model and experiment (fig. 2), lending confidence that the carbon budget is broadly accounted for. Accurately quantifying mixotroph grazing remains a perennial empirical challenge (Millette et al. 2023) and a significant source of uncertainty in biogeochemical models (Rohr et al. 2023), highlighting the need for further research in this area.
Empirical evidence suggests that the thermal response curve for growth rate has a different shape from that of competitive ability (i.e., minimum resource requirement), with higher temperatures favoring growth over competitiveness (Sunday et al. 2024). While our single population model does not account for competition, it is reasonable to expect shifts in the metabolic strategy in the presence of competitors toward increased investment to resource acquisition relative to growth. Higher temperatures might amplify this shift because the increased temperature sensitivity of growth increases efficiency and allows more energy to be devoted to resource acquisition.
Mixotrophs play an important biogeochemical role in the ocean (Mitra et al. 2014;Edwards 2019;Ward 2019). Contrary to the classical distinction between phytoplankton and zooplankton, mixotrophy is the default lifestyle of many plankton and may sometimes be the dominant metabolic mode in marine microbial food webs (Hartmann et al. 2012). Dynamic changes to respiration and the relative amounts of photosynthesis and grazing investment in a mixotroph's nutritional portfolio through adaptation are likely to have significant effects on net community production and carbon export (Mitra et al. 2014). In our simulated warming scenario (fig. 3), respiration integrated over the 150 days after the temperature change was 21% lower in the evolving population compared with the control. Photosynthesis in the evolving population was reduced by 13%, and grazing was increased by 8%. The observed change in grazing investment suggests that the mixotroph thermal response may affect food web dynamics through altered trophic relationships. Evolution, by reducing respiration, mitigates the short-term ecosystem perturbations (both biogeochemical and ecological) that result from the short-term plastic response to thermal stress. Evolving mixotrophs revert back toward the prechange metabolic state, displaying a resilience to temperature variability that stabilizes the metabolic strategy over evolutionary timescales.
The warming that we simulate in our model (67C) represents quite extreme heating in the context of climate change. Ocean surface temperatures are projected to increase by 0.867C to 2.897C during this century (Intergovernmental Panel on Climate Change Sixth Assessment Report; Fox-Kemper et al. 2021). However, the simulated warming is realistic when considering marine heat waves, which can warm the surface ocean by more than 107C and have lasted more than a year (Laufkötter et al. 2020). The short generation time of marine microbes, ranging from several hours to several days (Kirchman 2016), makes evolutionary changes within the duration of heat waves lasting several months (or longer) possible. The frequency and magnitude of marine heat waves may be more important in driving evolved changes to metabolic activity in marine mixotrophs than long-term average trends. If so, the trajectory of evolution may be characterized by temporary adaptive gains that are reversed when a heat wave is terminated.
In this study, we unite empirical data with mathematical models to explore the temperature-driven evolution of microbial respiration. We propose that increases in microbial respiration are the combined result of the direct effects of temperature on respiration rate and the indirect effects arising from physiological stress due to microbes being poorly adapted to their thermal environment. Our model suggests that evolution can partially compensate for increased respiration by shifting a population's thermal optimum to ameliorate the physiological stress. Parallel adaptive responses to warming in terrestrial systems (e.g., Oechel et al. 2000;Luo et al. 2001) suggest that this phenomenon might be broadly characteristic of microbial systems. If so, evolutionary modulation of respiratory demand may have cascading consequences for carbon cycling across many systems. Our ability to predict these biogeochemical responses would benefit from long-term studies evaluating how evolution alters thermal performance curves in microbial (and other) taxa over evolutionary timescales.