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 Q ₁₀ 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 Q ₁₀ 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 (Q ₁₀ = 1.8) than the direct thermal effect on biological rates (2 < Q ₁₀ < 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.

当前的气候变化威胁正在影响地热动物（Seebacher，White，＆Franklin，2014 ; Sunday，Bates，＆Dulvy，2012）导致人们对地热动物用于应对变暖的机制（Clark，Sandblom，＆ Jutfelt，2013； Seebacher等，2014）。在短时间尺度上，已知诸如鱼之类的放热动物可以通过多种方式调节其生理机能使其适应新的热环境（Seebacher等，2014）。这个过程发生在个人内部，目的通常是为了抵消直接的热效应，以在较大的温度范围内实现一致的功能（Einum等人，2019）。温度的急剧升高会在10度范围内将代谢率提高几倍（即高Q ₁₀值，这是10°C温度范围内的代谢率变化； Clarke，2004年； Seebacher等人，2014年））。通过数天至数周的热适应（Beitinger和Lutterschmidt，2011年），许多鱼类可以减少热驱动的代谢率增加（Seebacher等人，2014； Sumner＆Doudoroff，1938）。但是，这种适应通常不能完全消除对代谢率的热效应，使适应后的Q ₁₀值介于1.0和2.0之间（克拉克，2004年）。; Gräns​​等，2014；Sandblom，Gräns​​，Axelsson和Seth，2014年）。

通过适应（图1）获得的温水中增加的适应性通常不会转移到下一代（尽管仍未充分研究出跨代表观遗传效应的潜力）。因此，在日渐温暖的水域中，种群的持续热力性能需要通过演化改变热力性能曲线。在更长的时间尺度上，鱼类通过进化变化使其性能和代谢率适应各种温度（Clarke，2004）。代谢性状进化的证据来自大规模局部种群适应的证明（图1E），例如，大陆海岸线上的热梯度（Di Santo，2016； Healy＆Schulte，2012）。; Lucassen，Koschnick，Eckerle和Pörtner，2006；Sylvestre，Lapointe，Dutil和Guderley，2007年）或在不同的河支流中（Eliason等人，2011年）。然而，这种代谢特性的逆梯度变化（Conover和Schultz，1995年）并未得到普遍检测（例如，Alton，Conden，White和Angilletta，2016年），这表明我们尚未完全了解适应气候变暖将如何影响代谢特质。在更大的规模和热范围内，涵盖许多物种（每个物种在其自然温度下；图1F）的标准代谢率的种间比较表明，热效应要低得多（Q ₁₀ = 1.8）比直接热效应对生物发生率的影响（2 <  Q ₁₀  <3; Clarke，2004）。

尽管热适应实验和沿热梯度的局部适应在文献中相当普遍，但弥合单代之间的时间间隔和较深的演化时间的研究却很少（图1）。在实验室中，已经提出了某些代谢性状的跨代可塑性（Shama等，2016），但尚未得到充分探索。在无脊椎动物中进行了人工选择和实验进化实验，评估了各种性状的热适应性（Gilchrist＆Huey，1999年），包括在反梯度变化方向上的代谢适应性（Williams等，2016年）。）。在具有较长世代时间的较大生物中，进化实验的后勤过程显然更为复杂，但是已经在鱼类中进行了尝试（例如，Baer＆Travis，2000； Klerks，Athrey，＆Leberg，2019）。这样的实验对于测试热演化的速率和机理可能是有价值的。良好控制的实验室环境可以进一步降低发生混杂因素的风险（图1B）。

为了在短时间的实验室实验之外探索进化时标上的热适应问题，我们必须依靠自然或半自然系统。不幸的是，此类系统的描述很少。发电厂的热废水冷却水可以提供有趣的机会（White＆Wahl，2020年）。瑞典的一家核电站提供了一个半自然变暖的实验，并连续变暖了三十多年（图1C）。在一个大型自然围栏中栖息的河鲈（Perca fluviatilis）种群的水比周围种群的温度高5-10°C，这表明其氧气消耗量较低，这与新陈代谢速率的局部快速适应相符（Sandblom等，2016）。）。但是，该模型系统的人口混合水平和局部适应潜力尚未得到解决，因此热适应和适应的相对贡献仍不清楚。

在本期功能生态学中，Pilakouta等人。（2020年）描述了适应于冰岛地热加热湖泊的三棘刺背aster种群（Gasterosteus aculeatus）。这些棘背动物为探索许多有关鱼类热适应的问题提供了令人兴奋的机会（图1D）。直到大约一万年前冰岛的冰消退，棘背类海洋生物才开始入侵淡水系统（Einarsson等，2004）。这设置了本地适应时间的上限，并且三棘式棘背鱼的生成时间至少为一年（Östlund-Nilsson，Mayer和Huntingford，2006年）），世代数最多为10,000。

Pilakouta等。（2020）将适应于地热加热湖泊的这些独特的棘背类种群与适应寒冷生境的附近种群进行了比较。在测量标准代谢率和最大代谢率之前，对​​热适应和冷适应的鱼都应适应三个温度（10、15、20°C）。在所有适应温度下，适应温暖的种群通常显示出标准代谢率降低。由于这种效应已在多个湖泊中复制，这表明这是对stick背变暖的常见进化反应。该效果与先前在温暖适应的鱼类种群中进行热补偿的发现相符（例如，Sandblom等，2016； Sylvestre等，2007）。），这表明随着气候变暖，我们将在许多鱼类种群中看到这种普遍的反应。但是，这种适应的速度和幅度在物种和环境之间可能会有所不同。Pilakouta等人的研究结果的重要意义之一。（2020年）是在使用标准代谢率对变暖情景中的鱼类行为进行建模时，例如在估算能量消耗时，需要考虑这种适应性。

最大代谢率和有氧运动范围（最大和标准代谢率之差）的变化小于标准代谢率。温暖的鱼类可能被预测会在最高温度下增加有氧运动的范围，尽管变暖了也可以进行多个有氧过程（Clark等人，2013年），例如，可以提供足够的氧气输送来同化食物以供生长（Jutfelt等人。 ，2020），但未检测到该效果。如先前在温暖的鲈鱼中发现的那样，在温暖的适应性棘背found中发现了静息代谢率的可塑性和最大代谢率的刚性（图1C），这种现象被称为“塑料地板和混凝土天花板”现象（Sandblom等人）。等，2016）。

在还原主义者的实验室中，因素往往得到很好的控制。随着用于研究热适应的模型系统变得越来越自然（图1C–E），随着非生物和生物混杂因素越来越多地出现，温度只是其中的一个因素。例如，沿纬度或海拔线显示局部适应性的人群，可能还会面临重要因素的差异，例如光照，压力，氧气，栖息地，饮食和捕食。这些因素可能掩盖了温度的直接影响或模仿了这种影响。冰岛地热加热的湖泊模型系统（图1D）通过在较小的地理尺度上复制成对的异地和同胞种群而部分缓解了这一问题。混杂因素可能仍然包括例如温度的间接生态系统影响，可以模仿直接的热效应，而这可能很难被发现和解决。由于新陈代谢率朝着预测的方向变化，因此这种解释在这种情况下似乎不太可能（Pilakouta等人，2020年）。

除代谢率外，冰岛棘背stick种群目前还用于研究鱼类热适应的其他方面，例如行为热偏好（Pilakouta，Killen等，2019）和形态（Pilakouta，Humble等）。 。，2019）。使用该系统的未来研究可能还会解决有关观察到的热适应机制的问题。例如：相同的代谢途径是否适应，不同复制人群的潜在遗传变化是否相似？潜在的遗传和生化变化是否有多种途径产生观察到的高水平表型适应？在热适应过程中，除了新陈代谢还改变了其他表型性状吗？生态系统的影响如何与变暖的直接影响相互作用以引起观察到的进化？使用这种相对较新的温暖适应方法回答这些问题将有助于我们了解鱼类当前和未来对气候变化的适应情况。