Metabolic adaptation to warm water in fish
Fredrik Jutfelt
Abstract
The current threat of climate change impacts on ectothermic animals (Seebacher, White, & Franklin, 2014; Sunday, Bates, & Dulvy, 2012) has led to increasing interest in the mechanisms ectothermic animals employ to cope with warming (Clark, Sandblom, & Jutfelt, 2013; Seebacher et al., 2014). On short time-scales, ectothermic animals such as fish are known to thermally acclimate (or acclimatize) to a novel thermal environment by adjusting their physiology in many ways (Seebacher et al., 2014). This process occurs within individuals and the purpose is often to counter the direct thermal effect to allow consistent function over a large thermal range (Einum et al., 2019). An acute increase in temperature can elevate metabolic rates several-fold over a 10-degree range (i.e. high Q10 values, which is the change in metabolic rate over a 10°C temperature range; Clarke, 2004; Seebacher et al., 2014). Through warm acclimation over days to weeks (Beitinger & Lutterschmidt, 2011), many fish species can reduce that thermally driven increase in metabolic rates (Seebacher et al., 2014; Sumner & Doudoroff, 1938). However, such acclimation generally fails to completely abolish the thermal effect on metabolic rates, leaving post-acclimation Q10 values between 1.0 and 2.0 (Clarke, 2004; Gräns et al., 2014; Sandblom, Gräns, Axelsson, & Seth, 2014). Increased fitness in warm water gained through acclimation (Figure 1) generally does not transfer to the next generation (although the potential for transgenerational epigenetic effects remains underexplored). Continued thermal performance of populations in increasingly warmer waters therefore requires shifting thermal performance curves through evolution. On longer time-scales, fish adapt their performance and metabolic rates to various temperatures through evolutionary changes (Clarke, 2004). Evidence for evolution of metabolic traits comes from demonstrations of local adaptation of populations over large scales (Figure 1E), for example, thermal gradients along continental coastlines (Di Santo, 2016; Healy & Schulte, 2012; Lucassen, Koschnick, Eckerle, & Pörtner, 2006; Sylvestre, Lapointe, Dutil, & Guderley, 2007) or in different river tributaries (Eliason et al., 2011). Such countergradient variation (Conover & Schultz, 1995) in metabolic traits is, however, not universally detected (e.g. Alton, Condon, White, & Angilletta, 2016), suggesting we do not yet fully understand how adaptation to a warmer climate will affect metabolic traits. On an even larger scale and thermal range, interspecific comparisons of standard metabolic rates covering many species (each at their native temperature; Figure 1F) show that the thermal effect is much lower (Q10 = 1.8) than the direct thermal effect on biological rates (2 < Q10 < 3; Clarke, 2004). While thermal acclimation experiments and local adaptation along thermal gradients are fairly common in the literature, studies bridging the temporal gap between single generations and deep evolutionary time are rarer (Figure 1). In the laboratory, transgenerational plasticity has been suggested in some metabolic traits (Shama et al., 2016) but not fully explored. Artificial selection and experimental evolution experiments assessing thermal adaptation of various traits have been performed in invertebrates (Gilchrist & Huey, 1999), including metabolic adaptation in a countergradient variation direction (Williams et al., 2016). The logistics of evolution experiments are obviously more complex in larger organisms with longer generation times, but have been attempted in fishes (e.g. Baer & Travis, 2000; Klerks, Athrey, & Leberg, 2019). Such experiments, can be valuable for testing the rate and mechanisms of thermal evolution. Well-controlled laboratory environments may additionally reduce the risk for confounding factors (Figure 1B). For probing questions around thermal adaptation on evolutionary timescales beyond short laboratory experiments, we have to rely on natural or semi-natural systems. Descriptions of such systems are unfortunately rare. Thermal effluent cooling water from powerplants can offer interesting opportunities (White & Wahl, 2020). A nuclear power plant in Sweden provided one semi-natural warming experiment with continuous warming for more than three decades (Figure 1C). A population of perch Perca fluviatilis in a large natural enclosure receiving water 5–10°C warmer than that of the surrounding population showed lower oxygen consumption consistent with rapid local adaptation of metabolic rates (Sandblom et al., 2016). The level of population mixing and potential for local adaptation of this model system is, however, not resolved, and the relative contributions of thermal acclimation and adaptation are therefore still unclear. In this current issue of Functional Ecology, Pilakouta et al. (2020) describe populations of three-spined sticklebacks Gasterosteus aculeatus adapted to geothermally heated lakes on Iceland. These sticklebacks provide exciting opportunities for exploring many questions regarding thermal adaptation in fish (Figure 1D). The marine population of sticklebacks did not start to invade freshwater systems until the ice subsided from Iceland about 10,000 years ago (Einarsson et al., 2004). That sets an upper bound on the time available for local adaptation, and as the three-spined stickleback has a generation time of at least 1 year (Östlund-Nilsson, Mayer, & Huntingford, 2006), the number of generations is at most 10,000. Pilakouta et al. (2020) compared these unique stickleback populations adapted to geothermally heated lakes with nearby populations adapted to cold habitats. Both warm- and cold-adapted fish were acclimated to three temperatures (10, 15, 20°C) before measurements of standard- and maximum metabolic rates. The warm-adapted populations generally showed reduced standard metabolic rates at all acclimation temperatures. As this effect was replicated over multiple lakes, it suggests this is a common evolutionary response to warming in sticklebacks. The effect matches that of previous findings of thermal compensation in warm adapted fish populations (e.g. Sandblom et al., 2016; Sylvestre et al., 2007), suggesting this is a universal response that we will see in many fish populations as the climate warms. The rate and magnitude of such adaptation, however, will likely differ between species and contexts. One important implication of the findings in Pilakouta et al. (2020) is that modelling of fish performance in warming scenarios using standard metabolic rates need to account for this adaptation when for example estimating energy expenditure. Maximum metabolic rates and aerobic scope (the difference between maximum and standard metabolic rates) were altered less than the standard metabolic rates. Warm adapted fish could have been predicted to increase aerobic scope at the highest temperatures to allow multiple aerobic processes despite warming (Clark et al., 2013), for example, to provide sufficient oxygen delivery for assimilation of food for growth (Jutfelt et al., 2020), but that effect was not detected. Plasticity in resting metabolic rates and rigidity in maximum metabolic rates, as found in the warm adapted sticklebacks, was previously detected in the warmed population of perch (Figure 1C) and the effect was called the ‘plastic floors and concrete ceilings’ phenomenon (Sandblom et al., 2016). In the reductionist's laboratory, factors tend to be well controlled. As model systems for studying thermal adaptation get increasingly more natural (Figure 1C–E), temperature becomes but one factor out of many, as abiotic and biotic confounding factors increasingly appear. Populations showing local adaptation along latitudinal or elevational clines, for example, may also face differences in important factors such as light, pressure, oxygen, habitat, diet and predation. These factors may mask direct effects of temperature or mimic such effects. The Icelandic geothermally heated lake model system (Figure 1D) partially mitigates that issue through replicated pairs of both allopatric and sympatric populations at small geographic scales. Confounds may still include for example indirect ecosystem effects of temperature that could mimic direct thermal effects, which could be difficult to detect and account for. As the metabolic rates changed in the predicted direction, that explanation appears unlikely in this case (Pilakouta et al., 2020). In addition to metabolic rate, the Icelandic stickleback populations are currently being used to investigate other aspects of thermal adaptation in fish, such as behavioural thermal preference (Pilakouta, Killen, et al., 2019), and morphology (Pilakouta, Humble, et al., 2019). Future studies using this system may additionally address questions regarding the mechanisms underlying the observed thermal adaptation. For example: did the same metabolic pathways adapt and were the underlying genetic changes similar in the different replicate populations? Are there multiple routes of underlying genetic and biochemical changes producing the observed high-level phenotypic adaptations? What other phenotypic traits besides metabolism were altered during warm adaptation? How did ecosystem effects interact with the direct effects on warming to cause the observed evolution? Answering these questions using this relatively recent warm adaptation will help us understand ongoing and future adaptations to climate change in fish.