Understanding variation in metabolic rate

Metabolic rate reflects the ‘pace of life’ and is one of the most widely measured physiological traits. Metabolic rate has been linked to key physiological and life-history traits, including survival, growth, immunity, predation and reproductive output. Although metabolic rate is somewhat predictable – allometric scaling (see Glossary) between mass and metabolic rate is widespread, for example – variation is still substantial. Among species, there is a severalfold magnitude of difference in basal metabolic rate among individuals of the same mass (White and Kearney, 2013). At the level at which selection (see Glossary) operates (i.e. within species), basal metabolic rate can also vary considerably (Konarzewski and Ksiazek, 2013). This variation has long intrigued physiologists, and various hypotheses have been proposed to understand it (Glazier, 2005). As such, the field has been dominated by studies that seek to understand the proximal causes of variation – the biochemical and physiological mechanisms underlying the response of metabolic rate to biotic and abiotic drivers (as reviewed by Glazier, 2005). For example, the rate-of-living hypothesis (see Glossary) (Rubner, 1908) proposes that metabolic rate inversely determines longevity, based on observations that species with higher metabolic rates have shorter lifespans, although this remains controversial (Speakman, 2005; Glazier, 2015). More recent mechanistic explanations that seek to link metabolic rate to the pace of life have been proposed (Nilsson, 2002). The ‘compensation hypothesis’ (or ‘allocation hypothesis’) (see Glossary) suggests a fitness advantage of lower basal or standard metabolic rates as a result of lower maintenance costs, and thereby greater allocation of energy to reproduction (Gadgil and Bossert, 1970; Steyermark, 2002; Larivée et al., 2010). Alternatively, higher basal or standard metabolic rates allow for greater energy turnover and synthesis and maintenance of larger organs, leading to greater reproductive yield, known as the ‘increased-intake hypothesis’ (see Glossary) (Bennett and Ruben, 1979; Hayes et al., 1992). While these approaches may assign causation to an immediate response, exclusively mechanistic approaches have had limited success predicting how traits evolve. One key limitation with the mechanistic approach is that it lacks standardized methods to compare across studies. Meanwhile, phenomenological approaches such as those used in evolutionary biology are under-utilized in studies of metabolic rate.

Evolutionary biology seeks to understand the ultimate causes of variation in traits – causes that are a consequence of many generations of selection (Mayr, 1961). Darwin first observed that natural selection operating within populations ultimately shapes heritable differences among species. Estimates of the heritability (see Glossary) of metabolic rate vary widely, but are often more than zero (see ‘Genetic variation in metabolic rates’ section below). Within-population studies elucidate the selective forces acting on individuals, and the underlying genetic processes that constrain their evolution (see Glossary). To understand patterns in metabolic rates, and predict how they are likely to evolve under selection, it is necessary to measure ‘performance’ as fitness – the lifetime reproductive output of an individual – and to determine how fitness covaries with metabolic rates throughout ontogeny. Although metabolic rate is likely to evolve in response to selection, underlying genetic constraints may alter its evolution in ways that have yet to be considered in many physiological studies (Arnold, 1988), with some notable exceptions (e.g. Garland and Carter, 1994). We argue that quantitative genetics (see Glossary) provides a powerful framework for understanding the inheritance and evolution of traits, including their responses to selection.

Quantitative genetics partitions the population-level phenotypic variation of quantitative traits (see Glossary) into heritable and non-heritable components through measures of heritability and genetic correlation (see Glossary), and links those components to fitness via measures of selection. We emphasize three key advantages of studying metabolic rate within a quantitative genetics framework. (1) Formalism: evolutionary biologists have been thinking about the ultimate processes driving variation in traits since Darwin; microevolutionary theory and the powerful statistical tools developed from this work have been widely applied in the evolution community for over 40 years, and can be leveraged by physiological studies. (2) Predictive: microevolutionary approaches allow us to quantify how traits are likely to evolve given specific selection and genetic parameters. (3) Comparative: quantitative genetics provides standardized estimates of selection and heritability that are directly comparable among populations, species and environments. Here we advocate for wider adoption of the quantitative genetics approach in physiological studies in order to gain insights into evolutionary causes and consequences of variation in metabolic rate.

Selection is the phenotypic covariance between a trait and fitness, where fitness of an individual is determined by the contribution of offspring to the next generation (Falconer and Mackay, 1996). If fitness covaries with a trait, then that trait is said to be under selection. This relative difference in fitness among phenotypes (selection) forms one half of the breeder's equation and provides a standardized estimate of the strength and direction in which evolution is expected to occur, if the trait has adequate genetic variation. The slope of the relationship between relative fitness and a particular character (i.e. selection coefficient), weighted by the phenotype distribution, represents standardized estimates of selection.

In order to gain reliable estimates of selection, studies need to measure actual fitness (see Glossary). So far, selection studies on metabolic rate have relied almost exclusively on the use of fitness proxies, such as survival, growth or reproductive traits such as clutch size, rather than the ultimate measure of fitness: lifetime reproductive output (Box 2). This view is illustrated by the compilations of Biro and Stamps (2010), Burton et al. (2011) and White and Kearney (2013). The tables summarizing the known phenotypic correlations between metabolic rate and fitness proxies in these papers do not provide any examples of a correlation between metabolic rate and actual fitness.

Using fitness proxies can create misleading or incomplete interpretations of the strength and direction of selection if these proxies trade-off with actual fitness. For example, Pettersen et al. (2016) show that metabolic rates through ontogeny covary with actual fitness (lifetime reproductive output) as well as several fitness proxies, but the direction and magnitude of the covariance differs among measurements of metabolic rate. Fitness was maximized when individuals had low metabolic rates early in ontogeny (MR_E) but high metabolic rates later (MR_L) (or vice versa). Although we found evidence for correlational selection (see Glossary) alone based on true fitness, estimates based on fitness proxies incorrectly implied that directional selection was operating. For example, individuals with higher MR_E reproduced sooner, but individuals with lower MR_L were longer lived, and growth rate was maximized when MR_E was high and MR_L was low. In this case, using any of the commonly used proxies for fitness (growth rate, longevity, age at the onset of reproduction) would lead to wildly different, and incorrect, conclusions about the expected evolutionary trajectory of metabolic rate.

As the breeder's equation elegantly illustrates, selection on a trait will not generate evolution of that trait unless the trait is heritable. The capacity for metabolic rates to evolve thus depends not only on covariation between metabolic rate and fitness, but also on the other half of the breeder's equation – the genetic basis of variation in metabolic rate. The total phenotypic variance of a trait (V_P) is the sum of the variances attributable to genetic (V_G) and environmental (V_E) influences (including maternal effects), and the variance associated with the interaction between genetic and environmental influences (V_GE). V_G can be further subdivided into three components: additive (V_A), dominance (V_D) and interaction (V_I) variance, where collectively V_D and V_I are known as non-additive genetic variance and are not easily disentangled using standard quantitative genetics designs. V_A quantifies deviations from the mean phenotype attributable to the additive contribution of particular alleles to the phenotype; V_D quantifies interactions between alleles (dominance) and V_I quantifies interactions between alleles (epistasis). Heritability in the broad sense (H²) is calculated as V_G/V_P, whereas heritability in the narrow sense (h²) – the metric of interest for the breeder's equation – quantifies the contribution of additive genetic variance to total phenotypic variance and is calculated as V_A/V_P.

The heritability of a trait can be estimated in multiple ways (Box 3), but a common feature of all approaches is that they require the measurement of usually hundreds or thousands of individuals of known pedigree. The requirement to measure so many individuals means that estimates of h² for metabolic rate are historically rare, but are becoming much more common: we are aware of only two estimates published prior to 2000 (Lacy and Lynch, 1979; Lynch and Sulzbach, 1984), and most (43) of the remaining 64 estimates we were able to locate have been published since 2010. The available estimates range from 0 to 0.72; h² is significantly higher for endotherms than for ectotherms, and h² is significantly higher for active metabolic levels than for resting metabolic levels, defined here as the rate of oxygen consumption of an inactive, non-reproductive, post-absorptive animal (Box 4). These heritability estimates suggest that metabolic rate is, in many cases and especially for endotherms and for active metabolic rates, likely to be free to evolve under selection. In support of this suggestion, artificial selection experiments have yielded responses to selection on basal metabolic rate (Ksiazek et al., 2004) and maximum metabolic rate in laboratory mice (Gebczynski and Konarzewski, 2009; Wone et al., 2015), and maximum metabolic rate in bank voles Clethrionomys glareolus (Sadowska et al., 2015).

After accounting for genetic contributions to phenotypic variance, there remains a significant proportion of unexplained variation in metabolic rate that needs to be considered. Variation in metabolic rate may also be a consequence of environmental effects, which can affect metabolic rate either directly (e.g. temperature effects on metabolic rate in ectotherms; Angilletta et al., 2002), or indirectly (e.g. nutritional state on standard metabolic rate; Auer et al., 2015). Parental effects are also known to influence physiological traits (e.g. Bacigalupe et al., 2007; Sadowska et al., 2013). For example, brown trout may alter the routine metabolic rates of their offspring in order to control timing of emergence and therefore dispersal in larvae (Régnier et al., 2010). Addressing the relative importance of heritable versus non-heritable components of variation in metabolic rate will provide a more complete picture of how we expect variation in metabolic rate to evolve.

The univariate breeder's equation is a useful heuristic tool for understanding how microevolutionary processes (see Glossary) work. Increasingly, however, it seems that a more complex approach to predicting microevolution is necessary. The univariate breeder's equation necessarily treats each trait in isolation but it has long been recognized that no trait is an island (Dobzhansky, 1956). Traits covary with each other genetically such that evolution in one trait will necessarily cause evolution in another, and selection often acts on multiple traits simultaneously such that the fitness returns of one trait value depend on the value of other traits. The multivariate breeder's equation reflects this complexity and connectedness of traits in terms of both genetics and selection.

What is metabolic rate? Measures of metabolic rate integrate the rates at which organisms expend energy to do metabolic work, and so incorporate energy expenditure for a wide range of processes including the maintenance of homeostasis, growth and reproduction, movement and digestion. Metabolic rate is measured as the rate of heat production by direct calorimetry, or – more often – is estimated from rates of oxygen consumption or carbon dioxide production measured by indirect calorimetry (Lighton, 2008). Metabolic rate can be measured for animals that are free-living in the field; for animals at rest; for animals experiencing elevated metabolic rates due to exercise, digestion, lactation, thermogenesis or osmoregulation; or for animals exhibiting depressed metabolism due to hibernation or torpor, hypoxia or anoxia, desiccation or aestivation (Suarez, 2012). The major contributors to whole-organism metabolic rate will change as animals transition through these metabolic states, raising the important question of the extent to which they are constrained to always evolve together (‘metabolic rate’ is a single trait), or free to evolve independently (‘metabolic rate’ is many traits). In mammals, most metabolic activity during basal metabolism is associated with the internal organs including liver, kidney, gastrointestinal tract, heart and brain. Whereas during exercise-induced maximal metabolism, most (ca. 90%) metabolic activity is associated with work done by the locomotor muscles and the work done to deliver substrates and oxygen to these (reviewed by White and Kearney, 2013). From a mechanistic perspective, it therefore seems reasonable to conclude that these metabolic states represent different traits. From a quantitative genetics perspective, however, what matters is the extent to which two putative traits covary genetically. Published mass-independent additive genetic correlations between basal and running-induced maximal metabolic rate range from 0.21 to 0.72 (Dohm et al., 2001; Wone et al., 2009, 2015). Thus these traits – basal and maximal metabolic rate – are at least somewhat free to evolve independently, as has been demonstrated in selection experiments (Sadowska et al., 2015; Wone et al., 2015). What is less clear, however, is the extent to which measurements of a single metabolic state, but taken at different times, represent the same trait.

Two measurements of the same phenotype can be considered a single trait genetically only if they covary perfectly. Resting metabolic rate (as defined earlier) is perhaps the most widely measured physiological phenotype. Resting metabolic rate is repeatable (Nespolo and Franco, 2007; White and Kearney, 2013; Auer et al., 2016a) and heritable (see ‘Genetic variation in metabolic rates’ section above, and Box 4), but not perfectly so. It varies during ontogeny due to changes in size and growth (e.g. Moran and Wells, 2007; Rosenfeld et al., 2015), seasonally (e.g. Smit and McKechnie, 2010), geographically (e.g. Broggi et al., 2007), with food deprivation (e.g. Schimpf et al., 2012), due to changes mitochondrial coupling (Salin et al., 2015), and in response to a range of other biotic and abiotic variables (reviewed by Konarzewski and Książek, 2013; White and Kearney, 2013). Furthermore, not only does metabolic rate vary over time in the same individuals, but individuals can vary in the flexibility of their metabolic rate – in other words, the reaction norm of metabolic rate varies among individuals (Auer et al., 2015, 2016b). Thus an organism has no single metabolic rate, even for a single well-defined metabolic state (e.g. resting metabolic rate), and metabolic rate is therefore likely to be more than one single trait. Even if differences in metabolic rate throughout the life history were trivial, we know from a previous study that selection perceives metabolic rates (and their combinations) differently (Pettersen et al., 2016). In Pettersen et al. (2016), metabolic rate was only measured at two time points in the life history – both during early stages of development, which is unlikely to capture a complete picture of selection. We therefore suggest that the field should work towards gaining multiple measures of metabolic rate if we are to gain an accurate representation of net selection on metabolic rates. We acknowledge the considerable logistical challenges associated with doing so, but we nonetheless advocate treating metabolic rate at different times as separate traits as a useful heuristic for future studies.

Selection acts on combinations of traits, rather than individual traits in isolation (Lande and Arnold, 1983; Blows and McGuigan, 2015). Multivariate (or non-linear correlational) selection examines how selection affects, and is affected by, correlations between traits (Phillips and Arnold, 1989). Studies measuring selection on metabolic rate have largely focused on relationships between fitness and single traits (although see Artacho et al., 2015); however, univariate analyses provide limited scope for predicting change in phenotypic distribution (Phillips and Arnold, 1989). This is because apparent selection on one trait may be due to selection on another unmeasured, correlated trait – resulting in misleading conclusions about selection on the initial trait. Genetically coupled traits will not evolve independently; selection on one trait is likely to cause evolutionary changes in the other trait. For example, selection on metabolic rate early in ontogeny (MR_E) may yield a correlated response in metabolic rate late in ontogeny (MR_L) if MR_E and MR_L are positively genetically correlated, even if there is no direct selection on MR_L. Metabolic rate is known to show additive genetic correlations with a range of traits including body mass (Rønning et al., 2007; Nilsson et al., 2009; Tieleman et al., 2009; Careau et al., 2011; Schimpf et al., 2013), maximum metabolic rate (Sadowska et al., 2005; Wone et al., 2009, 2015), growth rate (Sadowska et al., 2009), the ability to cope with a poor diet (Sadowska et al., 2009) and exploratory behaviour (Careau et al., 2011). These and other additive genetic correlations may constrain the evolution of metabolic rate, but such constraints would not be identifiable in a univariate framework that considers metabolic rate in isolation. If several traits are measured, however, a multivariate approach can determine relative direct and indirect selection acting on each trait through multiple regression.

In a study on a bryozoan, Pettersen et al. (2016) found significant negative correlational selection between metabolic rates across two life stages [early (MR_E) and late (MR_L) in juvenile development], but positive phenotypic covariance between these traits (individuals with high MR_E generally possessed high MR_L and vice versa). In other words, there is a positive covariance between the two metabolic rates but selection ‘wants’ to decrease this covariance. Furthermore, under persistent correlational selection across generations, we might expect the positive covariance among metabolic rates to decrease and become negative over time. However, without an understanding of the degree of genetic covariance among traits (such as metabolic rates across ontogeny), our capacity to make such predictions remains limited.

Metabolic rate is perhaps the most widely measured physiological trait, and has long been argued to have important implications for life history, ecology and evolution. We argue that more widespread adoption of a microevolutionary quantitative genetics framework is valuable for understanding variation in metabolic rate. In adopting such an approach, we should consider metabolic rate as a multivariate trait and measure actual fitness (lifetime reproductive output) in the field, in order to estimate the genetic covariance between metabolic rates and fitness throughout ontogeny. Such measurements are needed in order to understand the drivers of phenotypic variation in metabolic rate.

The authors wish to thank Professor Neil Metcalfe and Professor Craig Franklin for thoughtful comments which significantly helped to improve the quality of this manuscript.