Is metabolic rate a universal 'pacemaker' for biological processes?

Biol. Rev. (2014), pp. 000–000. doi: 10.1111/brv.12115

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Is metabolic rate a universal ‘pacemaker’ for biological processes? Douglas S. Glazier∗ Department of Biology, Juniata College, Huntingdon, PA 16652, U.S.A.

ABSTRACT A common, long-held belief is that metabolic rate drives the rates of various biological, ecological and evolutionary processes. Although this metabolic pacemaker view (as assumed by the recent, influential ‘metabolic theory of ecology’) may be true in at least some situations (e.g. those involving moderate temperature effects or physiological processes closely linked to metabolism, such as heartbeat and breathing rate), it suffers from several major limitations, including: (i) it is supported chiefly by indirect, correlational evidence (e.g. similarities between the body-size and temperature scaling of metabolic rate and that of other biological processes, which are not always observed) – direct, mechanistic or experimental support is scarce and much needed; (ii) it is contradicted by abundant evidence showing that various intrinsic and extrinsic factors (e.g. hormonal action and temperature changes) can dissociate the rates of metabolism, growth, development and other biological processes; (iii) there are many examples where metabolic rate appears to respond to, rather than drive the rates of various other biological processes (e.g. ontogenetic growth, food intake and locomotor activity); (iv) there are additional examples where metabolic rate appears to be unrelated to the rate of a biological process (e.g. ageing, circadian rhythms, and molecular evolution); and (v) the theoretical foundation for the metabolic pacemaker view focuses only on the energetic control of biological processes, while ignoring the importance of informational control, as mediated by various genetic, cellular, and neuroendocrine regulatory systems. I argue that a comprehensive understanding of the pace of life must include how biological activities depend on both energy and information and their environmentally sensitive interaction. This conclusion is supported by extensive evidence showing that hormones and other regulatory factors and signalling systems coordinate the processes of growth, metabolism and food intake in adaptive ways that are responsive to an organism’s internal and external conditions. Metabolic rate does not merely dictate growth rate, but is coadjusted with it. Energy and information use are intimately intertwined in living systems: biological signalling pathways both control and respond to the energetic state of an organism. This review also reveals that we have much to learn about the temporal structure of the pace of life. Are its component processes highly integrated and synchronized, or are they loosely connected and often discordant? And what causes the level of coordination that we see? These questions are of great theoretical and practical importance. Key words: allometric scaling, biological regulation, body size, energy, information, metabolic theory, ontogenetic growth, pace of life, signalling, temperature. CONTENTS I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Information and energy use in living systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Metabolic theory: scaling the fire of life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) The metabolic theory of ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Problems with the metabolic pacemaker assumption of the MTE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Relationships between growth and metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Growth models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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* Address for correspondence (Tel: +1 814 641 3584; E-mail: [email protected]).

Biological Reviews (2014) 000–000 © 2014 The Author. Biological Reviews © 2014 Cambridge Philosophical Society

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(2) Cause versus effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Does metabolic rate drive growth rate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (b)Does growth rate drive metabolic rate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Relationships between metabolism and other biological processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Locomotor activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Food intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (3) Reproductive rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (4) Ageing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (5) Population growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (6) Rates of mutation and molecular evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (7) Species diversification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (8) Temperature effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (9) Beyond the MTE: the Adaptable Informed Resource Use model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Biological regulation: the importance of information for energy use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Recommendations for further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I. INTRODUCTION Understanding the ‘pace of life’ is of fundamental importance, because it is tied intimately not only to how organisms function at many levels of organization, but also to their ability to survive and reproduce in specific environments. Although the pace of life involves numerous biochemical, developmental, physiological, behavioural, and life-history processes with rates that often appear to be associated as a ‘syndrome’ (Ricklefs & Wikelski, 2002) or along a ‘single slow–fast axis’ (Versteegh et al., 2012), it is usually quantified as the rate of metabolism, which is most often estimated by the rate of oxygen consumption or carbon dioxide production, i.e. the ‘breath of life’. Moreover, it has long been assumed by many biologists that metabolic rate is the driver or ‘pacemaker’ for other biological processes (see e.g. Rubner, 1908; Crozier, 1924c; Pearl, 1928; Needham, 1933; Mitchell, 1962; Fenchel, 1974; McNab, 1980, 2002; Western & Ssemakula, 1982; Peters, 1983; Glazier, 1985; Buchakjian & Kornbluth, 2010; Clavijo-Baque & Bozinovic, 2012; Wiersma, Nowak & Williams, 2012; Martin, Ton & Niklison, 2013; Pontzer et al., 2014), thereby explaining why the pace of life often appears to involve a cluster of interrelated activities operating with largely parallel tempos. In fact, this metabolic pacemaker view is a central assumption of the highly influential ‘metabolic theory of ecology’ (MTE), as will be discussed below. However, and somewhat surprisingly, unlike other assumptions and predictions of the MTE, this fundamental assumption has received little or no critical analysis, including in several recent major reviews and forums concerning the MTE (Agrawal, 2004; O’Connor et al., 2007; Price et al., 2010, 2012; Sibly, Brown & Kodric-Brown, 2012). Therefore, the purpose of this review is to evaluate critically the commonly accepted belief that metabolic rate

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acts as a pacemaker for other biological processes. As a result, I argue that the pace of life cannot be completely understood without considering the effects of both energetic and informational control. Accordingly, I first briefly consider the importance of both energy and information in biological systems. Second, I give a concise overview of the MTE and the central importance (and limitations) of the metabolic-pacemaker assumption in this theory. Third, I evaluate the applicability of the metabolic-pacemaker assumption to various biological processes, with an emphasis on ontogenetic growth rate but also including rates of locomotor activity, food intake, reproduction, ageing, population growth, molecular evolution and species diversification. Temperature effects on rates of metabolism and other biological processes are also given special attention, as they are a cornerstone of the MTE and its many applications. Fourth, to illustrate the importance of informational control for the pace of life, I discuss several examples of our rapidly growing knowledge of how various genetic, cellular, and neuroendocrine systems co-regulate the rates of growth, metabolism and resource acquisition. Fifth, I make recommendations for further research on how biological regulation and adaptive optimization, in the context of various energetic and physical constraints, may determine variation in the pace of life and the relative harmony of its component processes.

II. INFORMATION AND ENERGY USE IN LIVING SYSTEMS Living systems require the effective acquisition and use of information (signals) and energy (resources), and their coordinated interplay. Indeed, the evolutionary success of various kinds of organisms depends


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critically on the complex network of interactions linking these fundamental processes. The living world is filled with organisms that have evolved information systems (genetic, cellular and/or neuroendocrine) that are well suited for managing the uptake and use of energy and matter in ways that promote the perpetuation of their own kind in specific environments. These regulatory systems in turn depend on energy and other resources for their continued operation. Metabolites themselves may even act as signals for the regulation of various cellular activities (also see Sections VI and VII). In short, living things are exquisitely ‘informed resource users’ (Glazier, 2009c, p. 22). However, many biologists have investigated the informational and energetic (metabolic) sides of life largely in isolation (but see Lane & Martin, 2010; Healy et al., 2013; Lane et al., 2013; Mathot & Dall, 2013). These dual aspects of life have even been used to distinguish two separate hierarchies of biological organization: a genealogical hierarchy from genes to higher taxa based on reproduction (genetic information transfer) versus an ecological hierarchy from proteins (enzymes) to ecosystems based on economics (energy/matter transfer) (Eldredge, 1985; Williams, 1992). For decades, information transmission (genetics) has been at the core of evolutionary theory (Fisher, 1930; Hamilton, 1964; Williams, 1966; Dawkins, 1976; Roff, 1997), whereas energy transformation (metabolism) has been at the core of ecosystem theory (Lindeman, 1942; Odum, 1968, 1971; Brown et al., 2004; Sibly et al., 2012). These different approaches have often continued to be followed separately to the present day even when the same biological system or phenomenon is being studied. For example, during the last two decades theoretical models of ontogenetic growth have appeared that emphasize either informational (regulatory) control (Nijhout, Davidowitz & Roff, 2006; Nijhout, Roff & Davidowitz, 2010) or energetic (resource) control (Kooijman, 2000; West, Brown & Enquist, 2001; van der Meer, 2006; Hou et al., 2008; Herman, Savage & West, 2011; Hou, Bolt & Bergman, 2011; Kearney & White, 2012; DeLong & Hanley, 2013) often with little or no recognition of the other (but see Callier & Nijhout, 2011, 2013). In this review I argue that the energy-based growth models are not only incomplete, because they ignore informational control, but also may be misleading. In particular, I contend that metabolic rate is not necessarily a ‘pacemaker’ for growth and other biological processes, as has been assumed in some of the most prominent of these models (West et al., 2001; Brown et al., 2004; Hou et al., 2008; Kerkhoff, 2012). Much evidence actually supports the view that rates of metabolism and other biological processes are co-regulated by genetic, cellular and neuroendocrine informational control systems. In short, understanding the rates of growth and other biological processes

requires knowledge not only of rates of energy use and how they may be physically constrained, but also of how biological information systems are used to manage the acquisition and allocation of energy in environmentally responsive and evolutionarily adaptive ways. Before going further, let me clarify how I am employing the terms ‘information’ and ‘metabolism’, which are frequently used, but rarely clearly defined. The concept of information has been used in many different ways by many different disciplines (Floridi, 2010), but here I focus on transmitted messages that alter dynamic living processes, where a message is a symbolic, physically expressed reflection of the state of an organism and (or) its environment. This biocentric definition is based on the concept of ‘meaningful information’ proposed by Reading (2011). I include within this definition two major kinds of biological information: (i) genetic codes (i.e. codical transmission: Williams, 1992) and associated epigenetic factors that direct the development of phenotypes, and (ii) biochemical, physiological and behavioural signals (i.e. signalling pathways) that direct the timing, rates, and magnitude of various kinds of biological processes. Genetic codes reflect past interactions between organisms and their environments in evolutionary time (see Maynard Smith, 2000). Those genes that have favoured organismal survival and reproduction in specific environments have been preferentially transmitted across generations. Biological signals reflect ongoing or recent changes in the state of an organism or its environment in physiological or behavioural time (see Gunawardena, 2008). Although these kinds of information largely involve different response times, they may interact during an organism’s lifetime when signalling pathways affect gene expression or vice versa, as may be expected in phenotypically plastic organisms (e.g. Smith, 1990; Bading, Ginty & Greenberg, 1993; Cardenas et al., 1999; Roberts et al., 2000; Brivanlou & Darnell, 2002; Gilbert & Epel, 2009; Proveniers, 2013; Sassone-Corsi, 2013). Furthermore, signalling pathways are adaptive, and thus the consequence of naturally selected codical transmission over evolutionary time. Metabolism can be defined generally as the physicochemical processes (including chemical reactions and various proton, electron, ion and metabolite fluxes: see Nicholls & Ferguson, 2013), specifically or collectively, that transform energy and materials into various living structures and activities. In my usage, metabolism supports not only cellular and tissue maintenance, but also additional vital activities, including growth, reproduction, locomotion and thermoregulation. This definition has important consequences for considering how metabolic rate interacts with the rates of other biological processes, as will be seen below (see Sections III–V). In short, to use the words of François Jacob (1973, p. 251) in a slightly altered way, metabolic energy provides ‘the power to do’, whereas information provides ‘the power to direct what is done’.


Douglas S. Glazier

4 III. METABOLIC THEORY: SCALING THE FIRE OF LIFE (1) The metabolic theory of ecology Recently, some investigators have re-emphasized the importance of energy metabolism in understanding ecological systems. In particular, the metabolic theory of ecology (MTE), which is largely a synthesis of pre-existing ideas and empirical data (O’Connor et al., 2007; Price et al., 2012; Glazier, 2013b), has been proposed as a basis for understanding the rates of various biological and ecological processes (Brown et al., 2004; West & Brown, 2005; Price et al., 2010; Sibly et al., 2012). According to the MTE, most of the variation in rate processes can be explained by differences in body size and temperature, as mediated by metabolic rate. Central to this theory is the assumption that metabolic rate is the pacemaker (driver) for all biological processes (Fig. 1). As Brown & Sibly (2012, p. 22) state: ‘the metabolic rate sets the pace of life, and the rates of all biologically mediated ecological processes’. This belief is based chiefly on the frequent observation that the scaling slopes of various rate processes in relation to body mass and temperature are similar to those of metabolic rate. In particular, the body-mass scaling slope in log-log space (the scaling exponent b in the power function R = aMb , where R is a rate process, a is the scaling coefficient, and M is body mass) often approximates 3/4, hence the so-called 3/4-power law (Peters, 1983; Savage et al., 2004b; Glazier, 2005; Anderson-Teixeira et al., 2009). The temperature scaling slope (negative apparent activation energy, −E of the Boltzmann-Arrhenius factor e−E/kT , where k is Boltzmann’s constant and T is temperature in kelvin) in Arrhenius plots also often approximates −0.65 eV for both metabolic rate and other biological processes (Gillooly et al., 2001; Brown et al., 2004). According to the MTE, the causes of both of these kinds of scaling are internalistic and physically constrained: body-mass scaling is dictated by the geometry and physics of internal resource-transport networks, whereas temperature scaling is governed by the kinetics of intracellular biochemical reactions. (2) Problems with the metabolic pacemaker assumption of the MTE There are six problems with the metabolic pacemaker assumption of the MTE. First, it is based empirically almost entirely on correlational analyses, which by themselves do not specify what is cause or effect. Although it has been assumed that metabolism dictates the rates of other processes, this has not been clearly demonstrated in a mechanistic way (O’Connor et al., 2007). It is also possible that the rates of various processes and their energy demand dictate the metabolic rate that is required (Glazier, 2005, 2013b; Glazier et al., 2011). For at least some biological processes, the metabolic rate

Fig. 1. How the pace of life may be constrained by energetic and other physical factors. This schematic representation is based on the metabolic theory of ecology (MTE). Note the one-way flow of control (indicated by arrows) from body size and temperature (acting independently according to physical laws) to metabolic rate (life’s pacemaker) to the rates of other biological processes, which then have varied ecological effects. The resource-transport network (RTN) constrains the supply of resources to metabolizing cells according to the MTE.

may be a ‘pace-enabler’ rather than a pacemaker per se. I will justify this point in Section IV by focusing on the process of growth, and in Section V by considering other biological processes. Second, proponents of the MTE have sought support for the pacemaker assumption by relating the body-mass scaling of maintenance metabolism (assumed to follow a 3/4-power law) to that of other biological and ecological processes. However, the metabolism of organisms engaged in routine or strenuous activities (e.g. field or active metabolic rates) consists of more than tissue maintenance, and its scaling with body mass may be significantly different from that for maintenance metabolism (e.g. basal or resting metabolic rates) (Nagy, 2005; Glazier, 2008, 2009a, 2010; Speakman & Król, 2010; Hudson, Isaac & Reuman, 2013), which has not been adequately considered by the MTE, notwithstanding an attempt by Gillooly & Allen (2007) (see critical analyses by Glazier, 2008, 2009a; White et al., 2008). Third, although the universality or predominance of the 3/4-power law for basal or resting metabolic rate has been commonly accepted for decades (Hemmingsen, 1960; Peters, 1983; Calder, 1984; Schmidt-Nielsen, 1984; Savage et al., 2004b; Moses et al., 2008), in recent years this belief has been eroding as an increasing number of exceptions are being reported. Across taxonomic groups, the interspecific metabolic scaling slope (b) varies extensively (mostly between 2/3 and 1) (Glazier, 2005, 2010; Reich et al., 2006; Seibel, 2007; White, Cassey



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& Blackburn, 2007; Makarieva et al., 2008; DeLong et al., 2010; Huete-Ortega et al., 2012; White, Frappell & Chown, 2012). The variation in b values is even more extensive for intraspecific analyses. In animals 3/4-power scaling does not appear to be as common as has been claimed (Moses et al., 2008). For example, Glazier (2005) has shown that 50.2% of the b values for the 642 intraspecific metabolic scaling relationships that he surveyed are significantly different from 3/4, increasing to 72.7% for datasets with body-mass ranges spanning two or more orders of magnitude, and to 88.5% for datasets with body-mass ranges exceeding 2.5 orders of magnitude (Glazier, 2010). Thus, for the statistically most reliable datasets (at least with respect to body-mass range, and assuming methodological error is similar across studies), 3/4-power scaling is much more likely to be rejected than accepted. Only a few studies of intraspecific metabolic scaling have been carried out in plants, but they also document significant variation in b (Chen & Li, 2003; Peng et al., 2010; Kutschera & Niklas, 2011). Moreover, evidence is growing that variation in b, both within and among species, can be linked to various physiological and ecological factors (Glazier, 2005, 2006, 2010; Terblanche, Janion & Chown, 2007; Hoque et al., 2010; Killen, Atkinson & Glazier, 2010; Vaca & White, 2010; Glazier et al., 2011; McFeeters et al., 2011; Müller et al., 2012; Ohlberger et al., 2012; Carey, Sigwart & Richards, 2013; White & Kearney, 2013), thus questioning the MTE’s assumption that the direction of causality is from metabolism to other processes, rather than the reverse (also see Sections IV and V). Fourth, recent comparative studies designed specifically to test the MTE’s assumption that the body-mass or temperature scaling of various biological processes is related to that of metabolic rate have revealed evidence both for (Brown et al., 2004; Savage et al., 2004b; Duncan, Forsyth & Hone, 2007; Marbá, Duarte & Agusti, 2007; Moses et al., 2008; Price et al., 2010; McClain et al., 2012; Sears et al., 2012) and against this assumption (Duncan et al., 2007; Marbá et al., 2007; Lovegrove, 2009; Terblanche et al., 2009; Terblanche & Anderson, 2010; Clark & Farrell, 2011; Coomes, Lines & Allen, 2011; McClain et al., 2012; Pawar, Dell & Savage, 2012; Rüger & Condit, 2012; Sears et al., 2012; White et al., 2012; Clauss et al., 2013; Lemaˆıtre, Müller & Clauss, 2014; also see Sections IV and V). Unfortunately none of these studies have been experimental, and thus the causes of parallel (or non-parallel) scaling patterns remain largely unknown. Fifth, recent comparative studies have shown that the effect of temperature on the rates of metabolism and other processes can vary considerably with estimated apparent activation energies (E) ranging mostly from 0.2 to 1.2 (Downs, Hayes & Tracy, 2008; Irlich et al., 2009; Knies & Kingsolver, 2010; Dell, Pawar & Savage, 2011; Rall et al., 2012). The metabolic rates of some kinds of organisms (e.g. some turtles and various calcified

marine invertebrates) may even be relatively invariant with changing temperature (E < 0.2: Penick et al., 1998; Watson et al., 2014). Terblanche et al. (2007) have also shown that E values can vary greatly within individuals and are not repeatable over time, thus calling into question a simple mechanistic explanation as specified by the MTE. Furthermore, the observations by Irlich et al. (2009) and Dell et al. (2011) that mean E is near the value of 0.65, as predicted by the MTE, may be biased by an under-representation of data from high latitudes where E values in damselflies are often much higher than 0.65 (Nilsson-Örtman et al., 2013). In addition, Arrhenius plots of several biological processes may show nonlinear relationships with temperature that contradict the linear prediction of the MTE (e.g. Crozier, 1924a,b; Crozier & Stier, 1926; Crozier, Pincus & Renshaw, 1935; Knies & Kingsolver, 2010; Dell et al., 2011; Ehnes, Rall & Brose, 2011; Kubiˇcka, Starostová & Kratochvíl, 2012). The effects of temperature on rate processes may not always be as expected from simple thermodynamic effects on biochemical reaction kinetics. Rates of metabolism and other processes may not merely passively respond to changes in temperature, nor do so similarly as specified by the metabolic pacemaker assumption (see below), but also may be adaptively adjusted in specific environmentally sensitive ways (Penick et al., 1998; Clarke, 2004, 2006; Clarke & Fraser, 2004; O’Connor et al., 2007; Dell et al., 2011; Marshall & McQuaid, 2011; also see Section V). Sixth, the MTE assumes that metabolic rate is a universal pacemaker, and therefore changes in the rates of all biological processes in relation to changes in body size and specific environmental factors should not only be similar to that of metabolic rate, but also to one another. Although near 3/4- and 1/4-power scaling with body mass is common for the rates and durations of many kinds of biological processes, respectively (Peters, 1983; Savage et al., 2004b; Hendriks, 2007; Sibly et al., 2012), there are many exceptions (see Peters, 1983; Martin, Genoud & Hemelrijk, 2005; Duncan et al., 2007; Glazier, 2010; Hui et al., 2012; Scott, Marsh & Hays, 2012; Clauss et al., 2013; Lemaˆıtre et al., 2014; and also Section IV). Furthermore, although fundamental biological processes, such as growth, differentiation and metabolism, often operate in a coordinated fashion during ontogeny, they can be dissociated (Needham, 1933). Growth (accumulation of somatic mass) and differentiation (formation of new structures, functions and body shapes) can be disengaged by various internal and external factors both naturally and experimentally. Growth can occur without differentiation (as in giants: Head & Polly, 2007), and differentiation can occur without growth (as in dwarfs: Needham, 1933; Beamer & Eicher, 1976). Many studies have documented the disengagement of growth from differentiation or sexual maturation as a result of hormonal action (Needham, 1933;


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6 Beamer & Eicher, 1976; Shea et al., 1990; Aubert et al., 1993; Fowden, 1995; Gilbert, 2000; Huang & Brown, 2000; Rose, 2005; Nijhout et al., 2006), genetic differences (Needham, 1933; Ishikawa & Namikawa, 1987), artificial selection (Drickamer, 1983), and changes in nutrition (Needham, 1933; Winick, 1961; McCance & Widdowson, 1962; Tanner, 1963; Glass & Swerdloff, 1980), photoperiod (Taranger et al., 2006) and temperature (Pechenik et al., 1990; Jobling, 2002; Forster, Hirst & Woodward, 2011; Forster & Hirst, 2012). Indeed, the well-known ‘temperature-size rule’ (maturation at smaller sizes at higher temperatures) seen in many ectotherms has been explained as the result of higher temperatures speeding up development (differentiation) more than growth (Atkinson, 1994; van der Have & de Jong, 1996; Forster et al., 2011; Forster & Hirst, 2012; Zuo et al., 2012). In addition, several intraspecific studies have shown that the pattern of response of metabolic rate to temperature change is often (but not always) different from that for growth rate, reproductive rate, food intake, and other biological processes (e.g. Needham, 1933; Brett, 1971; Christian & Wiebe, 1974; Barko & Smart, 1981; Burel et al., 1996; Barrionuevo & Burggren, 1999; Person-Le Ruyet et al., 2004; Chopelet, Blier & Dufresne, 2008; Marshall et al., 2011; Cruz et al., 2013; Hayes et al., 2014; but see Leffler, 1972; Caron, Goldman & Dennett, 1986). Interspecific mean slopes of the thermal response gradients of various biological processes (estimated as -E; see Section III.1) are also significantly heterogeneous (Dell et al., 2011). Although many developmental processes in plants respond similarly to temperature, their responses are distinctly different from those of photosynthetic rate and the activities of various enzymes involved in carbon metabolism (Parent et al., 2010; Parent & Tardieu, 2012), thus indicating a temperature-induced dissociation between metabolism and development. Salinity changes may result in a decoupling of the rates of growth and metabolism in fish, as well (e.g. Morgan & Iwama, 1991; Woo & Kelly, 1995; Swanson, 1998; McKenzie et al., 2001). These numerous observations of the dissociability of various rate processes contradict the metabolic pacemaker assumption, which requires that metabolism and other biological processes should respond in unison to environmental changes (see also Forster et al., 2011).

Some recent prominent growth models also assume that metabolic rate drives growth rate (see below). (1) Growth models Growth models may be descriptive (phenomenological) or mechanistic with specified causal pathways. Mechanistic growth models have focused on information-based (regulatory) or energy-based (metabolic) control. Both types of mechanistic models are relevant to my evaluation of metabolic rate as a biological pacemaker and will therefore be discussed below. Information-based growth models emphasize the role of biological regulation in determining growth rate and adult body size (Weiss & Kavanau, 1957; Kavanau, 1960; Calow & Townsend, 1981; Sibly & Calow, 1986; Ricklefs, 2003; Nijhout et al., 2006; Lui & Baron, 2011; Nijhout & Callier, 2013). Feedback mechanisms involving various kinds of signalling pathways (genetic, cellular, developmental and neuroendocrine) permit organisms to guide their growth actively toward optimally targeted final adult sizes. By contrast, energy-based growth models view growth as the passive result of the influence of other energetic processes, especially assimilation and metabolism. Differences in the elevation of ontogenetic scaling relationships for resource uptake and maintenance (or routine) metabolism determine the amount of energy available for growth, and where these relationships intersect determines final adult size (the body mass at which growth stops). Several growth models based upon the scaling of metabolism and (or) resource uptake have been proposed (e.g. von Bertalanffy, 1957; Sebens, 1982; Reiss, 1989; Kooijman, 2000; West et al., 2001; Hou et al., 2008; Quince et al., 2008; Kearney & White, 2012; Ohnishi et al., 2012), but here I focus on those (within the MTE family of models) that assume that metabolic rate is the pacemaker for growth (West et al., 2001; Hou et al., 2008). These models predict that growth rate should follow 3/4-power scaling like metabolic rate, both of which are considered to be constrained by the physical properties of internal resource-transport networks (West, Brown & Enquist, 1997; Banavar et al., 2010). (2) Cause versus effect

IV. RELATIONSHIPS BETWEEN GROWTH AND METABOLISM Growth is an excellent process to examine the validity of the metabolic pacemaker assumption because it is inextricably related to metabolism, which is in turn related to body size, a product of growth (note that this three-way linkage among growth, metabolism and body size is not explicitly recognized by the MTE).

The models of West et al. (2001) and Hou et al. (2008) assume that metabolic rate dictates growth rate, but the reverse may also occur. As pointed out in Section III, current empirical support for the metabolic pacemaker assumption is based chiefly on similarities between the body-mass and temperature scaling slopes of metabolic rate and those of growth and other biological processes. However, such similarities may arise in four, not necessarily mutually exclusive ways: metabolism drives growth; growth drives metabolism; growth and



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metabolism affect each other by reciprocal feedback; and (or) growth and metabolism are similarly, but independently related to a third factor or set of factors. I consider the first two possibilities in this section, and their equivalents for other biological processes in Section V. The third and fourth possibilities are considered in Section VI. (a) Does metabolic rate drive growth rate? Most of the current evidence supporting the hypothesis that metabolic rate drives ontogenetic growth is indirect (circumstantial). Apparent evidence includes similarities between the scaling exponents of growth (or production) and metabolism, which have been reported for some intraspecific (Peng et al., 2010; Glazier et al., 2011; Sears et al., 2012) and interspecific analyses (Peters, 1983; Brown et al., 2004; Glazier, 2009b). In mammals durations of various developmental periods (e.g. gestation and age at first reproduction) have also been reported to scale to approximately the 1/4 power (Peters, 1983; Calder, 1984; Wootton, 1987), as expected if they were related to the scaling of metabolism (Brown et al., 2004), but recent phylogenetically informed analyses have shown significant deviations from this expectation (Duncan et al., 2007; Clauss et al., 2013; cf . Sibly, 2012), as well as no significant correlations between basal metabolic rate and growth rate, age at first reproduction, and the durations of gestation and lactation, after removing the effect of body size (Kalcounis-Rueppel, 2007; Lovegrove, 2009; but see Symonds, 2005, who found a significant negative correlation between metabolic rate and gestation time in mammalian insectivores). Similarly in passerine birds, embryonic period is unrelated to mass-specific embryonic metabolism (Martin et al., 2013). These negative results call into question a direct link between the rates of development and metabolism (Lovegrove, 2009; Clauss et al., 2013). Contrary to the metabolic pacemaker view, growth, differentiation and metabolism can be dissociated (Needham, 1933; Chopelet et al., 2008; Forster et al., 2011; Forster & Hirst, 2012; also see Section III). In addition, although the apparent 3/4-power scaling of growth in plants has been considered as support for the metabolic pacemaker assumption (Niklas & Enquist, 2001; Niklas, 2004; Vasseur et al., 2012), recent empirical evidence suggests that metabolic rate in plants (especially young rapidly growing plants) scales closer to 1 than 3/4 (Tjoelker, Oleksyn & Reich, 1999; Reich et al., 2006; Cheng et al., 2010; Mori et al., 2010; Peng et al., 2010), thus contradicting this assumption. In addition, the mass scaling of tree production appears to be quite variable and significantly related to various biotic and abiotic factors, and therefore is not easily explained as being a simple function of a supposed universal 3/4-power scaling of metabolic rate

(Hui et al., 2012). Moreover, even when observed, similarities between the scaling of growth and metabolism represent weak evidence for the metabolic pacemaker assumption, because they are also consistent with the three other possible causal pathways mentioned above. Somewhat stronger evidence includes experimental manipulations of the availability of metabolites for growth. For example, some plant studies have shown that growth is severely inhibited by blocking sucrose synthesis or reducing the availability of hexose substrates, either by the absence of light (Gibon et al., 2004), or by using reverse genetics to repress critical enzymes (Fernie et al., 2002; Chen et al., 2005; Rossouw et al., 2007). Similarly, numerous studies of animals and unicellular organisms have shown that restricting food or specific nutrients (and thus the availability of metabolites) stunts growth and development (e.g. McCance & Widdowson, 1962; Broach, 2012). However, one may argue that these studies merely show that growth requires energy and nutrients, as do all living processes. They do not determine clearly whether metabolic rate drives growth rate or is adjusted to it, when available metabolic resources are above a certain minimal level (cf . Meyer et al., 2007). Better, but still limited experimental evidence for the metabolic pacemaker view comes from the study of Derting (1989) who showed that, under ad libitum food conditions, juvenile cotton rats (Sigmodon hispidus Say and Ord) with thyroxine implants had higher rates of metabolism, food intake and growth than controls. Since thryoxine is known to stimulate metabolism, Derting (1989) suggested that a higher metabolic rate was driving the higher growth rate. However, this interpretation is not conclusive, because thyroxine may also have further effects on appetite, growth and development that are not merely indirect effects of stimulated metabolism (Higgs et al., 1982; McNabb & King, 1993; but see Sinervo & Dunlap, 1995). For example, there is evidence that thyroxine can enhance the production of growth hormone and other growth factors (McNabb & King, 1993; Rose, 2005; Khalil & Mousa, 2013; see also Section VI for a discussion of other examples of ‘hormonal pleiotropy’). Nevertheless, further research is needed to sort out the causal pathways involved in how thyroxine affects growth and associated activities. In addition, under food restriction, cotton rats with thyroxine implants had a higher metabolic rate, but slower growth rate than controls (Derting, 1989). Therefore, rates of growth and metabolism can be uncoupled (see also Section III). Although high growth rates often entail high metabolic rates (see below), this may not always be so: opposite associations have even been found (Medrano & Gall, 1976; Morgan & Iwama, 1991; Steyermark, 2002; Bayne, 2004; Álvarez & Nicieza, 2005; Careau et al., 2013; Robertsen et al., 2014). Another revealing approach has been to compare growth rate with digestive and aerobic metabolic capacities, involving the uptake and processing of food and


Douglas S. Glazier

8 oxygen, respectively (Blier, Pelletier & Dutil, 1997). For example, in the Atlantic cod (Gadus morhua L.), individual variation in growth rate is positively correlated with the activities of several digestive enzymes (Lemieux, Blier & Dutil, 1999), but this comparison does not elucidate what is causing what. To help resolve this problem, Blier, Lemieux & Devlin (2002) compared coho salmon (Oncorhynchus kisutch Walbaum) individuals whose growth was enhanced by transgenic growth hormone with more slowly growing nontransgenic controls. They found little or no difference in digestive enzyme activity and intestinal tissue mass between the transgenic and control animals. Therefore, Blier et al. (2002) concluded that growth rate in coho salmon is not limited by digestive metabolic capacity. Ricklefs, Starck & Konarzewski (1998) arrived at a similar conclusion for birds, noting that digestive capacity is highly flexible and responds to demand, as is also true in other animals (Hammond et al., 1994; Piersma & Lindström, 1997; Ricklefs, 2003; Blier et al., 2007; Zaldua & Naya, 2013). Alternatively, perhaps growth rate in fish and other animals is limited by aerobic metabolic capacity, as advocated by Pauly (2010). However, the analyses of Blier et al. (1997) indicate that the aerobic capacity of fish is more than ample to meet the energy costs of muscular protein synthesis, a major component of fish growth; although this may not be so if fish are also engaged in other energetically expensive activities (e.g. swimming: see Pauly, 2010). Other studies suggest that aerobic metabolic capacity does not limit growth rate in squid (O’Dor & Hoar, 2000) and the crustacean Daphnia magna Straus (Chopelet et al., 2008), as well. Although experimentally lowered oxygen levels can retard growth and development in various animals (e.g. Pichavant et al., 2001; Pauly, 2010; Callier & Nijhout, 2011, 2013; Callier et al., 2013), elevated oxygen levels have also been observed to decrease, not increase growth rate in the largemouth bass Micropterus salmoides (Lacépède) (Stewart, Shumway & Doudoroff, 1967) and the fruit fly Drosophila melanogaster Meigen (Callier et al., 2013), which is unexpected if aerobic capacity is limiting growth. In addition, other fish typically show no increase in growth or metabolic rates when exposed to supersaturated oxygen levels (e.g. Edsall & Smith, 1990; Doulos & Kindschi, 1992; Foss, Evensen & Øiestad, 2002; Person-Le Ruyet et al., 2002; Thorarensen et al., 2010; Yang et al., 2014; but see Dabrowski et al., 2004; Hosfeld et al., 2008). And in response to hyperoxia, adult body size decreases, remains the same, or increases slightly in various insects (Harrison, Kaiser & VandenBrooks, 2010). When body-size increases are observed, they are mainly the result of extended development time, rather than increased growth rate (Harrison et al., 2010). Conclusive evidence that growth rate in any animal is limited by metabolic capacity, when abundant food and oxygen is available, has yet to be presented. More likely evidence

may be found in unicellular organisms whose growth and metabolism are highly linked (see Fenchel & Finlay, 1983; Sonnleitner & Käppeli, 1986; Glazier, 2009b). However, in most organisms growth rate appears to be adaptively optimized at levels below the maximum that is physiologically possible (Calow & Townsend, 1981; Arendt, 1997; Dmitriew, 2011). (b) Does growth rate drive metabolic rate? The second hypothesis that growth (or production) drives metabolism (see Daan, Masman & Groenewold, 1990; Tilman et al., 2004) is not only consistent with reported similarities between the scaling of growth and metabolism, but also has additional comparative and experimental support. This hypothesis predicts that ontogenetic changes in growth rate should cause temporally associated shifts in the metabolic scaling exponent (b) (Glazier, 2005). Three major lines of evidence support this prediction. First, in many animals and plants, b tends to be higher during early developmental stages when growth is rapid than during later developmental stages when growth has slowed down (Riisgård, 1998; Glazier, 2005; Hunt von Herbing, 2006; Czarnołeski et al., 2008; Peng et al., 2010; Rombough, 2011; Gaitán-Espitia et al., 2013; Jensen et al., 2013). Second, in many pelagic animals with high growth or production rates throughout their relatively short lives (e.g. salps, squid, jellyfish, ctenophores, pteropods and krill), b remains relatively high, often approaching 1 (Glazier, 2005, 2006). Third, in the cockroach Blatella germanica L. growth is relatively slow during instars 1–4, but rapid during instars 5 and 6, and as expected b shifts from a low (0.71) to high value (1.29) (Woodland, Hall & Calder, 1968), a pattern that is remarkable because it is the reverse of the high to low b pattern more commonly seen in animals (Glazier, 2005). Since growth of new tissue is expected to be proportional to existing tissue mass, the energy cost of growth should scale with body mass to the power of 1 (Wieser, 1994). In support, heat production accompanying feeding (the specific dynamic action, SDA), which has been associated with the cost of biosynthesis, scales near 1 in a variety of animals (Secor, 2009). Therefore, the metabolic scaling exponent should ‘approach 1, when metabolic costs of growth predominate’ (Jørgensen, 1988, p. 322), as has been frequently observed in rapidly growing organisms (Teissier, 1931; Tjoelker et al., 1999; Glazier, 2005; Montes et al., 2007; Czarnołeski et al., 2008; Rombough, 2011; also see Section VI). Metabolic profiling studies provide further evidence that ‘growth drives metabolism and not vice versa’ (Meyer et al., 2007, p. 4762). In the plant Arabidopsis thaliana Heynh, biomass accumulation is correlated negatively with the concentrations of various metabolites centrally involved in biosynthesis, but positively with those involved in other activities (e.g. stress responses). According to Meyer et al. (2007), these results suggest



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that growth depletes metabolites needed for biosynthesis, rather than being enhanced by their increased availability (also see Sulpice et al., 2009). Similar results have been found for the tomato, Solanum pennellii Correll (Schauer et al., 2006). Numerous other studies have revealed that metabolism responds to the anabolic needs of cell proliferation, as mediated by various signalling pathways (Vander Heiden, Cantley & Thompson, 2009; Agathocleous & Harris, 2013; also see Section VI). In addition, the view that growth may drive metabolism is consistent with recent theoretical and empirical evidence that metabolic fluxes may commonly be regulated by demand (product formation and need), as well as by supply (substrate availability) (also see Section VII). Experimental evidence also supports the hypothesis that growth drives metabolism, rather than (or in addition to) the reverse. First, manipulations of animal embryos have shown that increases in growth (cell division) rate increase metabolic (respiration) rate, but increases in metabolic rate have no effect on growth rate (Tyler, 1942). Second, temperature-induced elevation of basal metabolic rate does not affect testis growth in great tits, Parus major L. (Caro & Visser, 2009). Third, enhancement of growth by transgenic growth hormone causes zebra fish [Danio rerio (F. Hamilton)] to have higher metabolic rates than those of controls (Rosa et al., 2008). Fourth, in captive zebra finch chicks (Taeniopygia guttata Reichenbach) a low-protein diet followed by a high-protein diet results in compensatory (‘catch-up’) growth that significantly elevates adult basal metabolic rate (Criscuolo et al., 2008; but see Chin et al., 2013). Fifth, nutritionally enhanced growth rate in mealworms (Tenebrio molitor L.) results in an increase in the metabolic scaling exponent b (Teissier, 1931), as expected. Sixth, steeper metabolic scaling similarly results from artificial selection for increased body size and thus growth rate in the edible snail Helix aspersa (O. F. Müller) (Czarnołeski et al., 2008). Seventh, a natural experiment involving the freshwater amphipod Gammarus minus Say shows that populations with low postmaturational growth, apparently due to size-selective fish predation, exhibit significantly lower b values (0.54–0.62) than those of populations with relatively high postmaturational growth that are not exposed to fish predators (0.76–0.77) (Glazier et al., 2011). The above observations are understandable, because growth entails a metabolic cost (Wieser, 1994), and so as growth rate increases so should metabolic rate (Vleck & Vleck, 1980; Parry, 1983; Jobling, 1985; Jørgensen, 1988; Wieser, 1994; Riisgård, 1998; Peterson, Walton & Bennett, 1999; Sadowska et al., 2009; Careau et al., 2013), unless there are compensating increases in growth efficiency (Medrano & Gall, 1976; Bayne, 2004; Pace et al., 2006; Tamayo et al., 2011) or trade-offs between growth and maintenance metabolism under

resource-limited conditions (Wieser, 1994; Steyermark, 2002; Burton et al., 2011; Reid, Armstrong & Metcalfe, 2011; Careau et al., 2013; Hayes et al., 2014). Since growth rate varies with body size, the cost of growth may also affect metabolic scaling (Parry, 1983; Riisgård, 1998; Peterson et al., 1999; Glazier, 2005; Montes et al., 2007; Czarnołeski et al., 2008; Glazier et al., 2011; Gaitán-Espitia et al., 2013). Although Hou et al. (2008) include the cost of growth (biosynthesis) in their growth model, they do not explicitly consider that this cost may cause growth to affect metabolism, rather than (or in addition to) the reverse causation that they emphasize. Increased growth and its energy demand often require an increase in metabolic rate, but an increase in metabolic rate need not increase growth rate, because increased metabolic energy can be used by a variety of other vital activities as well.

V. RELATIONSHIPS BETWEEN METABOLISM AND OTHER BIOLOGICAL PROCESSES According to the MTE, metabolic rate drives the rates of not only growth, but also those of all other energy-dependent biological processes (Brown et al., 2004; Brown & Sibly, 2012). However, again the empirical support for this assumption is based chiefly on claimed similarities between the body-mass or temperature scaling of metabolism and that of other processes, which may or may not occur (see references cited in Section III). The frequent finding of near 3/4-power scaling of the rates of various biological processes and near 1/4-power scaling of the duration of various biological events in birds and mammals is suggestive of some common metabolic control (Savage et al., 2004b; Sibly, 2012). However, other explanations for these scaling patterns are possible (e.g. Lindstedt & Calder, 1981; Ginzburg & Damuth, 2008; Glazier, 2010). Moreover, the metabolic pacemaker interpretation is complicated by the results of several recent studies indicating that (i) the metabolic scaling exponent in endothermic vertebrates is significantly less than 3/4 (nearer to 2/3) and changes with body-size interval in mammals (Hayssen & Lacy, 1985; Painter, 2005; White, Phillips & Seymour, 2006; Clarke, Rothery & Isaac, 2010; Kolokostrones et al., 2010; and other references cited in Glazier, 2005, 2010, 2013a), and (ii) some critical life-history durations in mammals (e.g. lifespan, gestation period and age at first reproduction) appear to scale with a power significantly less than 1/4 (Martin et al., 2005; Duncan et al., 2007; Clauss et al., 2013; Lemaˆıtre et al., 2014) (note that if the scaling of metabolic rate and life-history durations were related, a metabolic scaling exponent < 3/4 should be accompanied by exponents for life-history durations that are >, not < 1/4 as observed) (also see Section IV). In addition, we still know little about the scaling of various


Douglas S. Glazier

10 biological processes in other animals, which may yield further surprises [e.g. see Scott et al. (2012) and Dillon & Frazier (2013) for evidence that development time in insects and squamate reptiles may also scale with an exponent < 1/4]. (1) Locomotor activity As already discussed with respect to growth (see Section IV), the MTE also fails to acknowledge that the energy demands of various biological processes may determine the metabolic rate that is required, causing effects on metabolic scaling, rather than vice versa (Glazier, 2005, 2013b). For example, increased rates of locomotor activity result in increased metabolic rates and metabolic scaling exponents (b), both within (Glazier, 2005, 2009a; Snelling et al., 2011) and across species of various kinds of animals (Glazier, 2005, 2008, 2010; Niven & Scharlemann, 2005; Weibel & Hoppeler, 2005). As activity increases, b increases towards 1, apparently because of the increased influence of muscle power production, which scales isometrically with mass, on total metabolism (Glazier, 2005, 2008, 2010). Locomotor activity is voluntary, and thus it does not increase merely because of increased metabolic rate, but rather its increase elicits the body to increase metabolic rate (and other supporting processes such as heart and breathing rates) to sustain its energy demand [as mediated by the energy sensor, adenosine monophosphate-activated protein kinase (AMPK): Jørgensen, Richter & Wojtaszewski, 2006; also see Section VI]. Since higher activity levels require more metabolic machinery, one can predict increases in basal or resting metabolic rate (BMR or RMR) as well (see Daan et al., 1990; Koteja, 2000; Speakman & Selman, 2003; Seibel, 2007; Biro & Stamps, 2010; Killen et al., 2010; Burton et al., 2011; Norin & Malte, 2012; Wiersma et al., 2012; Konarzewski & Ksia¸ zek, ̇ 2013; Zhang et al., 2014; but see Swanson et al., 2012; Dawson et al., 2013; Galliard et al., 2013). As expected, positive genetic associations have been observed between BMR and level of exploratory activity in deer mice [Peromyscus maniculatus (Wagner)] (Careau et al., 2011), and between BMR and exercise-induced aerobic capacity in bank voles [Myodes glareolus (Schreber)] (Sadowska et al., 2005) and laboratory mice (Wone et al., 2009). Similar interspecific associations between BMR and behavioural activity have been found among Peromyscus (and other neotomine rodent) species (Glazier, 1985; Mueller & Diamond, 2001; Careau et al., 2011) and between Sorex and Crocidura shrews (von Merten & Siemers, 2012). Feng, Zhao & Lu (2014) also report that BMR and locomotor speed are positively correlated in mammals generally. However, genotypic associations between RMR and exercise capacity may not be universal (see Niitepõld, 2010). Glazier (2005) further discusses how ontogenetic changes in other processes such as reproduction and

thermoregulation may affect metabolic rates and their scaling with body mass. (2) Food intake Empirical evidence for the MTE’s proposed causal links between metabolism and other biological processes or phenomena is also meagre. For example, the scaling exponents (b) for the rates of food intake and metabolism are often similar in animals (often near 2/3 or 3/4) (Peters, 1983; Reiss, 1989; but see Pawar et al., 2012), but a detailed study on mammalian herbivores has revealed that b for food-intake rate can be explained without invoking metabolic rate (Shipley et al., 1994). Shipley et al. (1994) concluded that it is an open question whether the biomechanical properties constraining the rate of oral food processing have adjusted to match metabolic demand, or the reverse. Pawar et al. (2012) have also shown that consumption rates of free-living animals depend on search-space dimensionality, resulting in mean b values of 0.85 and 1.06 in two- and three-dimensional habitats, respectively, that differ significantly from the 0.75 power expected for metabolic rate. Further evidence indicates that metabolic rate does not necessarily drive food-intake rate, but conversely increased food intake and associated activities may elevate metabolic rate, both in the short term as the heat increment of feeding (or SDA: see Secor, 2009), and in the long term as a higher BMR, because of enhanced metabolic machinery related to foraging activities and food processing (Daan et al., 1990; Lindström & Kvist, 1995; Speakman & McQueenie, 1996; Koteja, 2000; Selman et al., 2001; Nilsson, 2002). For example, an experimental manipulation of brood size and thus parental feeding rate significantly increased BMR in the marsh tit Parus palustris (L.) (Nilsson, 2002). The higher BMR of pregnant and lactating versus non-reproductive mice (Mus musculus L.) is also associated with a higher feeding rate and larger metabolically expensive supporting structures, such as the alimentary tract, liver, kidneys, heart and lungs (Speakman & McQueenie, 1996). In addition, artificial selection for high food intake in mice resulted in a significantly higher BMR and associated liver mass and intestinal length (Selman et al., 2001), and similarly, selection for high BMR was associated with higher food intake and size of internal organs ̇ Czerniecki & Konarzewski, 2009; Konarzewski (Ksia¸ zek, ̇ 2013). & Ksia¸ zek, (3) Reproductive rate The above observations may additionally help to explain positive interspecific correlations that have been found between BMR and reproductive output in mammals (e.g., McNab, 1980, 2002, 2012; Glazier, 1985; White & Seymour, 2004; but see Kalcounis-Rueppel, 2007), but their cause-and-effect relationship is still an open question. High metabolic rates may drive or enable



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high reproductive outputs or alternatively increases in both maintenance and reproductive energy expenditures may be linked to a third factor, e.g. food abundance (Glazier, 2009d). However, intraspecific associations between resting metabolic rate and reproductive performance are varied (Burton et al., 2011), having ´ et al., 2013; Sadbeen reported to be positive (Boratynski ´ ¸ nski & Konarzewski, 2013), absent (Earle owska, Gebczy & Lavigne, 1990; Hayes, Garland & Dohm, 1992; Johnston et al., 2007; Schimpf, Matthews & White, 2012), or even negative (Blackmer et al., 2005). Experimentally enlarged litter sizes result in increased BMR in lactating females of the common vole Microtus arvalis (Pallas) (Hürlimann et al., 2014), thus showing that increased reproductive energy demand can stimulate metabolic rate, rather than vice versa as expected by the metabolic pacemaker assumption. (4) Ageing The MTE embraces the ‘rate of living’ theory, which posits that longevity is inversely related to metabolic rate - i.e. living fast means living short (Pearl, 1928). However, empirical evidence contradicting this classical view has been increasing (Selman et al., 2012; Speakman & Garratt, 2014; but see Rollo, 1995; Seyahooei, van Alphen & Kraaijeveld, 2011; Hou, 2013). It is certainly true that within many groups of animals, smaller species have higher metabolic rates and shorter lifespans than larger species. However, lifespan is not a simple function of metabolic rate, and is related to many other biological features that may or may not correlate with body size (Austad, 2010). For example, despite their small body size and high metabolic rates, many bats and birds have unusually long lives, which can be explained in terms of reduced mortality rates associated with flight, as well as reduced ageing rates resulting from relatively low generation of and increased protection from free radicals and other reactive oxygen species (ROS) associated with aerobic metabolism (Munshi-South & Wilkinson, 2009; Austad, 2010). In addition, amphibians with protective toxins tend to live longer than non-toxic species, independently of their body size (Blanco & Sherman, 2005; Hossie et al., 2013), although whether differences in metabolic rate are involved is not yet known. Moreover, recent statistical analyses have shown that maximal lifespan in various vertebrates is unrelated to resting metabolic rate after controlling for differences in body size and taxonomic affinity (Speakman, 2005; de Magalhães, Costa & Church, 2007; Robert, Brunet-Rossinni & Bronikowski, 2007; Valencak & Ruf, 2007; Furness & Speakman, 2008; Montgomery, Hulbert & Buttemer, 2012b). At the intraspecific level, although resting metabolic rate is negatively associated with lifespan in the garter snake Thamnophis elegans (Baird & Girard), as expected from the rate of living theory (Bronikowski & Vleck, 2010), it is unrelated to longevity in the fruit fly D. melanogaster (Van Voorhies, Khazaeli & Curtsinger,

2004) and the Glanville fritillary butterfly Melitaea cinxia (L.) (Niitepõld & Hanski, 2013) and to the rate of senescence in the great tit P. major (Bouwhuis, Sheldon & Verhulst, 2011), and is even positively related to lifespan in dogs (Speakman, Van Acker & Harper, 2003) and laboratory mice (Speakman et al., 2004), the opposite of that expected. An additional finding that contradicts the rate of living theory is that peak metabolic rate during flight is positively, rather than negatively correlated with lifespan in M. cinxia (Niitepõld & Hanski, 2013). Furthermore, ROS production is highly variable among tissues and species and is apparently not directly proportional to metabolic rate (Hulbert et al., 2007; Robert et al., 2007; Burton et al., 2011; Boardman et al., 2012; Selman et al., 2012; Speakman & Garratt, 2014; but see Hou, 2013) or in some cases to longevity as well (Lewis et al., 2012; Montgomery, Hulbert & Buttemer, 2012a; but see Archer et al., 2012; Munro et al., 2013), thus undermining a key proposed link between metabolic rate and longevity, although other mechanisms may be involved (Van Raamsdonk et al., 2010; Pulliam, Bhattacharya & Van Remmen, 2012). The cave salamander Proteus anguinus Laurenti highlights this problem because it has an unusually long lifespan (mean = 68.5 years), despite having a small body size (15–20 g), and an unexceptional metabolic rate and levels of antioxidant activity and age-related cellular damage (Voituron et al., 2011). (5) Population growth The MTE further posits that an increased metabolic rate can increase the rate of population growth (Brown et al., 2004). As predicted, maximal population growth rate (r max ) often scales with body mass to near the −1/4 power (Fenchel, 1974; Peters, 1983; Savage et al., 2004a; Reiss & Schmid-Araya, 2010; but see Frazier, Huey & Berrigan, 2006; DeLong et al., 2010), and with temperature according to a slope near −0.65 (Savage et al., 2004a; but see Dell et al., 2011). However, empirical support for a mechanistic connection between the rates of metabolism and population growth is still lacking. Does elevated metabolic rate increase the rates of ontogenetic growth, development and reproduction, thus shortening generation time and increasing reproductive output, both of which in turn increase r max (cf . Savage et al., 2004a)? This seems plausible, but as I pointed out earlier in this section, evidence for a faster metabolic rate increasing reproductive output is equivocal. In addition, more evidence supports ontogenetic growth driving metabolism, rather than the reverse (see Section IV.2). Furthermore, although body-size adjusted analyses have shown that gestation time is inversely correlated with RMR in mammals (McNab, 1980; Symonds, 1999; but see Kalcounis-Rueppel, 2007) and a cockroach (Schimpf et al., 2012), growth rate and age at first reproduction appear to be unrelated to BMR in mammals (Symonds, 1999; Kalcounis-Rueppel, 2007; Lovegrove, 2009). Therefore, the available evidence provides little


Douglas S. Glazier

12 or no support for the view that metabolic rate drives population growth rate, although these rates may be related in other ways (e.g. both may be related to relative resource abundance). (6) Rates of mutation and molecular evolution According to the MTE, an increased metabolic rate may even increase the rate of evolution and diversification of species via its enhancement of mutation rate. However, most of the tests of this metabolic theory of evolution use indirect evidence that is often contradictory (see also Gillman & Wright, 2013). For example, the MTE predicts that like metabolic rate, evolutionary rate should covary negatively with body size, but positively with temperature. These predictions are supported by several studies (Martin & Palumbi, 1993; Gillooly et al., 2005; Allen et al., 2006; Estabrook, Smith & Dowling, 2007; Fontanillas et al., 2007; Lanfear et al., 2013; Lourenço et al., 2013), but contradicted by many others (Gissi et al., 2000; Held, 2001; Thomas et al., 2006; Liow et al., 2008; Gillman, McCowan & Wright, 2012; Lourenço et al., 2013). Moreover, because many factors besides metabolic rate covary with body size, it is difficult to determine the mechanistic basis for correlations between body size and evolutionary rates (see Bromham, 2009; Lanfear et al., 2013; Lourenço et al., 2013). For example, in plants the negative association between body size and the rate of molecular evolution appears to be mediated by the rate of mitosis, rather than by metabolic rate (Lanfear et al., 2013). Similarly, temperature effects on mutation rate may not be mediated by metabolic rate, but by other processes (e.g. species-specific responses to physiological stress: Matsuba et al., 2013). In addition, direct tests for associations between RMR and the rates of mutation or molecular evolution have usually produced nonsignificant results (Mooers & Harvey, 1994; Bromham, Rambaut & Harvey, 1996; Gissi et al., 2000; Lanfear et al., 2007; McGaughran & Holland, 2010; Feng et al., 2014). However, although the rate of molecular evolution of poison dart frogs also varies independently of RMR, it appears to be positively correlated with active metabolic rate (Santos, 2012). In any case, the proposed mechanism by which metabolic rate may affect mutation rate, namely ROS production, appears to be absent in mammalian cells (Hoffmann et al., 2004) and the nematode Caenorhabditis elegans (Maupas) (Joyner-Matos et al., 2011). Therefore, postulated relationships between metabolic rate and the rates of ROS generation and DNA mutation are presently ‘far from clear’ (Galtier et al., 2009). (7) Species diversification Tests of the MTE’s ability to predict patterns of species diversity (especially with respect to temperature, which is assumed to act via effects of metabolic rate on

mutation and speciation rates) have also met with mixed success, with evidence both for (e.g. Brown et al., 2004; Wang et al., 2009; Rombouts et al., 2011; Bailly et al., 2014) and against (e.g. Algar, Kerr & Currie, 2007; Hawkins et al., 2007; McCain & Sanders, 2010; Qian & Ricklefs, 2011). Moreover, predicted positive effects of temperature on species diversity often observed at large geographical scales may be reversed in specific taxa (e.g. marine invertebrates with direct development: Pappalardo & Fernández, 2014) and at small spatial scales (e.g. in springs: Glazier, 2012). The effects of temperature on species diversity may involve complex, sometimes opposing effects on not only mutation rate, but also rates of colonization, mortality, extinction, food production, resource use and species interactions, only some of which may be linked to metabolic rate (Glazier, 2012; Stegen, Ferriere & Enquist, 2012; Brown, 2014). In addition, convincing mechanistic evidence for critical steps of the MTE’s proposed causal pathway, especially from metabolic rate to mutation rate to speciation rate, remain to be obtained (see Qian & Ricklefs, 2011; Dowle, Morgan-Richards & Trewick, 2013; and Section V.6). (8) Temperature effects The most promising evidence for the metabolic pacemaker view is likely to come from studies of temperature effects and of physiological processes that are so closely linked to metabolism (respiration) that they are often used as surrogate measures of metabolic rate (e.g. heartbeat and breathing rate: see Millidine, Metcalfe & Armstrong, 2008; Green, 2011; Bishop & Spivey, 2013; but note that even variation in heartbeat may not always be congruent with that of metabolic rate developmentally, allometrically or in response to temperature change: Barrionuevo & Burggren, 1999; Clark & Farrell, 2011; Bruning et al., 2013). Temperature-induced increases in the rates of various biological and ecological activities (including rates of growth, reproduction and behavioural activity at the organismal level; and rates of population growth, species interactions, nutrient recycling and trophic energy flow at the ecological level) may be expected to be linked to increases in metabolic rate that fuel these processes. Accordingly, endothermic animals (e.g. birds and mammals) with relatively high body temperatures and metabolic rates generally have a faster pace of life than ectothermic animals (e.g. reptiles and amphibians) of comparable body size (McNab, 2002, 2012). Nevertheless, organisms are not complete slaves to the so-called ‘tyranny of Boltzmann’ (Clarke & Fraser, 2004, p. 247). They may adaptively adjust their activities to temperature changes in ways that are not predicted by simple Boltzmann–Arrhenius kinetics. For example, contrary to the prediction of the MTE, the marine snail Echinolittorina malaccana (Philippi) decreases its metabolic rate with increasing ambient temperature over a biologically tolerable



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range (30–40∘ C), apparently as an adaptive strategy to conserve energy under warm conditions (Marshall & McQuaid, 2011; for similar observations in other intertidal animals, see Newell & Northcroft, 1967). Furthermore, temperature influences the locomotor activity of this snail in a markedly different way than its metabolic rate (Marshall et al., 2011), which also contradicts the metabolic pacemaker view (also see Section III for citations of other examples of temperature-induced dissociations between metabolic rate and the rates of other biological processes). Other animals [e.g. black bears, Ursus americanus (Pallas)] conserve energy by depressing their metabolism in ways that cannot be explained by simple temperature effects (Heldmaier, 2011; Tøien et al., 2011; also see Section VI). In addition, marine pteropods can even increase their metabolic rate in response to lower temperatures, the opposite of that predicted by the MTE (Seibel, Dymowska & Rosenthal, 2007). However, the most telling example involves circadian rhythms whose adaptive timing of various life activities in many organisms remains unaffected by a wide range of physiological temperatures (Harmer, 2007; François, Despierre & Siggia, 2012), and by association a wide range of metabolic rates as well (see e.g. Abe et al., 1982). These and other examples of temperature compensation (Clarke, 2004, 2006; O’Connor et al., 2007; Terblanche et al., 2009; Hare et al., 2010; White, Alton & Frappell, 2011; Dymowska et al., 2012; Pyl et al., 2012) suggest that even when the rates of biological processes increase with increasing temperature as predicted by the MTE, biological systems may be actively permitting (rather than merely passively succumbing to) such responses, at least to some extent, because they increase the rates of fitness-related processes (e.g. rates of growth, reproduction, energy intake, and locomotor avoidance of predators) (cf . Frazier et al., 2006; Angilletta, Huey & Frazier, 2009). Therefore, effects of temperature on the pace of life may not be always and wholly physically inevitable, but rather may be, at least partially, the result of biological adaptation. This argument is supported by extensive evidence that the various processes making up the pace of life often exhibit different sensitivities to temperature (see Section III).

according to changing need (a demand-driven process), rather than simply (or only) controlling rate processes by how much energy and materials are provided (a supply-driven process). Metabolism is responsive and adjustable and not merely dictatorial in a physically constrained way. Therefore, the MTE seems to offer an incomplete, over-simplified view of the role of metabolic rate in the pace of life (see Fig. 1). I argue that we need a more complex, expanded view involving multi-directional interactions between the rates of metabolism and other processes (see Fig. 2, including the references cited in the legend, which document interactions that I have not had enough space to cover in this focused review). This more complex perspective, which I call the Adaptable Informed Resource Use (AIRU) model, requires an explicit consideration of how adaptive optimization and biological regulation (including genetic, cellular and neuroendocrine linkages and feedbacks between metabolism and other processes) may affect the pace of life. Metabolism appears to be inextricably embedded in a network of processes that are regulated collectively in highly sophisticated adaptable ways, as I describe in Section VI. Nevertheless, although the AIRU model is biologically more realistic than the MTE, one might argue that these models have different, even complementary explanatory objectives. The MTE (a purposely simple physical-constraint model) is a coarse-grained, large-scale attempt to predict the rates of a variety of biological processes using a minimal number of factors with large effects (i.e. body size and temperature) (Price et al., 2012). In many (but not all) instances, it has been surprisingly successful in doing so (Sibly et al., 2012). However, in my opinion, the MTE should continue to be used as a general predictive tool, only as long it is recognized that its underlying mechanisms are oversimplified, incomplete, and may even be partially or wholly incorrect. In particular, based upon evidence

(9) Beyond the MTE: the Adaptable Informed Resource Use model Although metabolic rate may be a biological pacemaker in at least some instances (e.g. those involving moderate temperature effects or closely related physiological responses), I have described several lines of evidence indicating that metabolic rate may be an enabler, rather than a pace-setter for the rates of several biological processes (e.g. growth, locomotion, and food intake), or may even be unrelated to the rates of some processes (e.g. ageing, circadian rhythms, and molecular evolution). Growing evidence suggests that metabolism facilitates many biological processes by providing fuel

Fig. 2. Legend on next page.


Douglas S. Glazier

14 given herein, I would argue that in any future applications of the MTE, metabolic rate should be regarded as merely an indicator of the pace of life, rather than as necessarily being the master driver of all other biological processes (also see Section VII). By contrast, the more complex AIRU model is a fine-grained attempt to identify a variety of causal pathways that can affect biological rates to various degrees and at many scales. Although its complexity reduces its general predictive power, because of the larger number of possible, contingent outcomes, nevertheless it may guide us towards attaining a more detailed and accurate mechanistic understanding of the multiple factors affecting the pace of life.

VI. BIOLOGICAL REGULATION: THE IMPORTANCE OF INFORMATION FOR ENERGY USE A major limitation of the MTE and other energy (resource)-centered models (e.g. dynamic energy budget theory: Kooijman, 2010) is that they typically ignore the role of information in regulating the use of energy and matter by living systems. Potential links between metabolic scaling and genome sizes (or rates of DNA

nucleotide substitution) have received some attention (e.g. Kozłowski, Konarzewski & Gawelczyk, 2003; West & Brown, 2005; DeLong et al., 2010), but these studies do not examine how genomes may specifically control metabolic rate and its scaling with organism size. Interconnections between biological regulation and metabolic scaling remain a largely unexplored territory (also see Hulbert & Else, 2004; Chaui-Berlinck et al., 2005; Suarez & Darveau, 2005; Bromage et al., 2012). In effect, the MTE and many other metabolic scaling models, as currently developed, essentially ignore half of the essence of life, which depends critically on both information and resources for its perpetuation (also see Section II). Here I argue that a complete understanding of how the pace of life (at many levels of biological organization) is determined must include not only physical constraints on the rates of energy supply and power production, but also the adaptable mechanisms by which various informational control systems regulate resource acquisition and expenditure. For heuristic purposes, I focus on the genetic, cellular and neuroendocrine regulation of growth, a fundamental biological process that is critically involved in the pace of life. Other vital processes that illustrate well the important role of biological regulation in energy acquisition and use include reproduction (Bronson, 1989; Evans & Anderson, 2012;

Fig. 2. How the pace of life and its many components may be co-regulated by interactive, energy- and information-based control systems. In this schematic representation of the Adaptable Informed Resource Use (AIRU) model proposed here (see Section V.9), note the multi-directional cause and effect relationships (indicated by double arrows) among body size, temperature, the rates of metabolism and other biological processes, and various other ecological factors and effects. Genetic, cellular and/or neuroendrocine systems are considered to play a role in all of the indicated interactions, as well as in others not shown (e.g. between ecological factors and metabolic rate, body size and temperature). Interactive effects between body size and temperature include the effects of temperature on growth (Atkinson, 1994; Daufresne, Longfellner & Sommer, 2009; Ohlberger, 2013) and the scaling of metabolic rate with body size (Glazier, 2005; Hoque et al., 2010; Killen et al., 2010; Ohlberger et al., 2012), and the effects of body size on thermoregulation (Schmidt-Nielsen, 1984; Helmuth, 1998; Seebacher, Grigg & Beard, 1999; McNab, 2002) and temperature scaling of various biological processes (Precht, Laudien & Havsteen, 1973). Interactive effects between body size and metabolic rate include the relative importance of resource supply versus demand on the scaling of metabolic rate, which depends on body-size-related factors (e.g. physical volume and surface area), the relative metabolic intensity of an organism (Glazier, 2005, 2008, 2010), and possibly age (Callier & Nijhout, 2012). Note also that the geometry of resource-transport networks (claimed by the MTE to account for the effect of body size on metabolic rate) may develop or evolve to support, rather than dictate metabolic scaling (Darveau et al., 2002; Kozłowski & Konarzewski, 2004; Weibel & Hoppeler, 2005; White & Kearney, 2013). Interactive effects between temperature and metabolic rate include kinetic effects on reaction rates, the body-warming effect of high metabolic rates, as well as co-regulated adaptive adjustments of body temperature and metabolic rate (Schmidt-Nielsen, 1984; Cossins & Bowler, 1987; Gillooly et al., 2001; McNab, 2002, 2012; Glazier, 2008, 2009a; Angilletta, 2009; Speakman & Król, 2010). Interactive effects between body size and the rates of various biological processes include non-metabolic effects (e.g. biomechanical and physical space limitations) (Schmidt-Nielsen, 1984; Hon˘ek, 1993; Glazier, 2000; Vogel, 2003), and the effects of rates of growth on body size (Blanckenhorn, 2000; Glazier et al., 2011). Interactive effects between temperature and the rates of various biological processes also include non-metabolic effects (e.g. physiological stress, changes in kinematic viscosity, and effects on diffusion rates) and the effects of various behavioural and physiological processes on thermoregulation (B˘elehrádek, 1957; Cossins & Bowler, 1987; Sidell & Hazel, 1987; Hunt von Herbing, 2002; Woods & Hill, 2004; Angilletta, 2009; Larsen & Riisgård, 2009; Beveridge, Petchey & Humphries, 2010; Humphries, 2013). Interactive effects between metabolic rate and the rates of other biological processes are discussed extensively in the text. Interactions between various ecological factors and other components of the model are also considered to be multi-directional (Cossins & Bowler, 1987; Blanckenhorn, 2000; McNab, 2002; Wolcott & Gaylord, 2002; Glazier, 2005, 2006, 2009b, 2010; Angilletta, 2009; Terblanche et al., 2009; Chown & Gaston, 2010; Killen et al., 2010; Burton et al., 2011; Glazier et al., 2011; McFeeters et al., 2011; Hui et al., 2012; Pawar et al., 2012; Ohlberger, 2013). Biological Reviews (2014) 000–000 © 2014 The Author. Biological Reviews © 2014 Cambridge Philosophical Society


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Garcia-Garcia, 2012; Schneider, Klingerman & Abdulhay, 2012), exercise (Holloszy, Kohrt & Hansen, 1998) and hibernation (Melvin & Andrews, 2009; Storey, Heldmaier & Rider, 2010; Florant & Healy, 2012). In particular, hibernating mammals can rapidly and substantially change their rates of metabolism and food intake regardless of their body mass or ambient temperature (Florant & Healy, 2012), a remarkable adaptive, regulatory ability unexplained by simple energetic models, such as the MTE. During torpor, decreases in metabolic rate are not merely passive responses to declines in body temperature (T b ), as predicted by the MTE (Gillooly et al., 2001), but are actively down-regulated (Heldmaier & Ruf, 1992; Storey et al., 2010; Heldmaier, 2011; Tøien et al., 2011). This view is supported by observations that (i) during the onset of torpor in hibernating mammals, reductions in metabolic (heat production) rate typically precede declines in T b , and (ii) these physiological shifts occur without marked increases in thermal conductance (heat loss) that would be required for hypothermy to cause decreased metabolic rates (Heldmaier & Ruf, 1992). Increases in metabolic (heat production) rate also cause increases in T b to prevent tissue freezing during torpor, and to reestablish normal thermoregulatory function during arousal from torpor (Heldmaier, Ortmann & Elvert, 2004; Storey et al., 2010). Furthermore, some mammals [(e.g. black bears and golden spiny mice Acomys russatus (Wagner)] can lower their metabolic rate substantially during torpor with only minor or negligible changes in T b (e.g. Tøien et al., 2011; Grimpo et al., 2013). After all, the adaptive value of torpor and hibernation is to conserve energy, which is best done by actively suppressing metabolic (heat production) rate. Temperature-induced reductions in metabolic rate would be more energy wasteful (and thus less adaptive) because they would require increased heat loss for lowering T b (Heldmaier & Ruf, 1992). Therefore, a lowered T b , if it occurs, appears to be largely an incidental result of reduced metabolic rate, although temperature effects on metabolic rate may also occur (Geiser, 2004; Storey et al., 2010; Heldmaier, 2011). The MTE-related growth models of West et al. (2001), Hou et al. (2008) and Zuo et al. (2012) posit that growth is the passive result of energy fluxes whose rates are physically constrained by body size and temperature (see also Section IV.1). These models appear to be effective at predicting some general properties of growth, especially when the focus is on broad comparisons among many different kinds of species. However, recent studies indicate that the model of West et al. (2001) is not universally applicable, because it provides poor fits to the growth trajectories of many kinds of pelagic marine invertebrates (Hirst & Forster, 2013) and crop plants (Shi et al., 2013). In addition, the MTE-related growth models may have limited use at the intraspecific level, because growth does not appear to be merely driven by

metabolic rate as assumed, but rather these processes appear to be co-regulated in ways that are sensitive to both the available resource supply and the metabolic demand that is required. Various genetic, cellular and neuroendocrine studies support the view that rates of growth, metabolism and resource assimilation are regulated in highly integrated ways. Three important kinds of interrelated mechanisms appear to be involved in this coordinated regulation: genetic pleiotropy and correlations, hormonal pleiotropy and integrated signalling networks, and feedback responses. Genetic associations between growth, metabolism and food intake may cause them to evolve and be expressed in adaptive concerted ways. I have already noted that artificial selection for increased food intake is correlated with increased metabolic rate in laboratory mice (Selman et al., 2001). Other selection studies and comparisons of different genetic strains of clams, insects, fish, mice and cattle also appear to show genetic correlations among growth, food intake and metabolism (Medrano & Gall, 1976; Bishop & Hill, 1985; Silverstein, Wolters & Holland, 1999; Valente et al., 2001; Herd, 2009; Sadowska et al., 2009; Tamayo et al., 2011). However, further studies are needed to elucidate the genetic architecture of growth and its relationship with various metabolic processes. Six major kinds of studies show promise: (i) genetic correlation analyses (Nespolo et al., 2005; Sadowska et al., 2009; Wilson et al., 2010), (ii) quantitative trait locus (QTL) studies (Mackay, 2004; Schauer et al., 2006; Meyer et al., 2007); (iii) artificial selection studies (Selman et al., 2001; Davidowitz, Nijhout & Roff, 2012); (iv) genome-wide gene expression studies using manipulated cultures (Brauer et al., 2008; Sulpice et al., 2013) or different growth genotypes (Gonzalez et al., 2010; Meyer & Manahan, 2010; Rhee et al., 2013); (v) studies on imprinted genes involved in coordinating growth, metabolism and nutrient acquisition (Smith, Garfield & Ward, 2006; Charalambous, da Rocha & Ferguson-Smith, 2007); and (vi) studies on oncogenic mutations, which influence cellular metabolism and proliferation in multiple, integrated ways that promote cancerous growth (Jones & Thompson, 2009; Vander Heiden et al., 2009; Cairns, Harris & Mak, 2011). Many hormones affect multiple traits and processes, a property that has been called ‘hormonal pleiotropy’ (Flatt, Tu & Tatar, 2005; Bartke, 2011). Hormonal pleiotropy appears to be involved importantly in the coordinated control of growth, metabolism, and food intake. For example, growth hormone (GH) not only stimulates somatic growth, but also various metabolic pathways supporting it (Leggatt et al., 2009; Møller & Jørgensen, 2009; Bartke, 2011). As a result, tilapia (Oreochromis sp.) with transgenic growth hormone exhibit not only enhanced growth rates, but also higher resting metabolic rates than controls (McKenzie et al., 2003). Furthermore, GH is part of an elaborate regulatory


Douglas S. Glazier

16 network that includes other important hormones and regulatory factors both up- and downstream from it. For example, upstream in the network is the hormone ghrelin, which not only stimulates GH secretion, but also food intake (Wren et al., 2000; Castañeda et al., 2010). Downstream are various regulatory factors such as insulin-like growth factor-1 (IGF-1), which has multiple effects on growth, metabolism and other biological processes (Powell-Braxton et al., 1993; Dantzer & Swanson, 2012; Swanson & Dantzer, 2014). The effect of ghrelin on food intake is also mediated by AMPK, a key cellular energy sensor that has manifold effects on metabolism and body mass (Kahn et al., 2005; Jørgensen et al., 2006; Hardie, Ross & Hawley, 2012). Target of rapamycin (TOR) complexes, which interact with insulin, AMPK and other growth factors, play an important role in integrating cellular metabolism, growth and proliferation, as well (Hay & Sonenberg, 2004; Sarbassov, Ali & Sabatini, 2005; Caldana et al., 2013; Konarzewski & Ksia¸ zek, ̇ 2013). Furthermore, various components of the above complex regulatory system are highly sensitive to several kinds of internal and external conditions, including food (nutrient) availability, vigorous exercise, and other biotic and abiotic stressors, thereby enabling the adaptive allocation of energy to various homeostatic and biosynthetic functions as needed (Hay & Sonenberg, 2004; Sarbassov et al., 2005; Jørgensen et al., 2006; Dantzer & Swanson, 2012; Hardie et al., 2012; Konarzewski & Ksia¸ zek, ̇ 2013). Intricate regulatory networks also appear to coordinate plant growth with carbon supply (Sulpice et al., 2009; Smeekens et al., 2013), and cell division with nutritional and metabolic status (Brauer et al., 2008; Buchakjian & Kornbluth, 2010; Broach, 2012; Cai & Tu, 2012). The ability of growing organisms to achieve targeted adult body sizes, even when perturbed by various environmental stressors, suggests that feedback regulation is involved. Mathematical models have been developed that show how this growth control may work (Weiss & Kavanau, 1957; Kavanau, 1960; Calow & Townsend, 1981; Nijhout et al., 2006), and empirical evidence for the postulated feedback mechanisms has been accumulating steadily (Nijhout et al., 2006; Lui & Baron, 2011; Mirth & Shingleton, 2012; Callier & Nijhout, 2013). Larval insect growth appears to be regulated in part by the hormone ecdysone, which increases in concentration at a critical prepupal body size, thus causing simultaneous cessation of feeding and growth (Nijhout et al., 2006, 2010). Other hormones (juvenile and prothoracicotropic hormones) and growth factors (insulin-like peptides) also play a role in governing insect body size and its response to nutrition and temperature (Nijhout et al., 2006, 2010; Mirth & Shingleton, 2012; Callier & Nijhout, 2013). In mammals, after a certain amount of growth is reached, cell proliferation is increasingly inhibited, apparently by the down-regulation of many growth-promoting genes. Different organs appear to use

different kinds of feedback mechanisms to accomplish growth limitation: e.g. muscle growth is negatively regulated by myostatin, which increases in concentration as a function of organ size; liver growth is affected by bile acid flux, a function that is dependent on organ size; and pancreas growth appears to be limited by a cell-counting mechanism (Lui & Baron, 2011). By sensing intracellular metabolites, cells can also use feedback control on signalling networks to coordinate cellular growth, division and metabolism (Broach, 2012; Cai & Tu, 2012; Ward & Thompson, 2012). Evidence is also accumulating that plants and animals can regulate their organ size by compensatory coordination of cell multiplication and enlargement (Neufeld et al., 1998; Horiguchi & Tsukaya, 2011). Although much more needs to be learned, the above evidence already strongly suggests two major conclusions: (i) growth is not limited merely by energetic or other physical constraints in a passive way, but instead is actively controlled by sophisticated informational regulatory systems that are sensitive to whole body or organ size and other internal and external conditions; and (ii) it is unlikely that metabolism is simply a driver of growth, but rather is intimately integrated with growth, both in an energetic sense (as a provider of fuel and substrates) and in an informational sense (as part of a regulatory network that adaptively orchestrates the growth, metabolism and nutrient acquisition of organisms in environmentally sensitive ways that maximize evolutionary fitness). In this section, I have emphasized how informational control systems can regulate energy use, but conversely nutrients, metabolites and energy use can also affect endocrine function and the action of various signalling networks, as is becoming increasingly appreciated (MacKenzie, VanPutte & Leiner, 1998; Buchakjian & Kornbluth, 2010; Gerosa & Sauer, 2011; Cai & Tu, 2012; Eveland & Jackson, 2012; Agathocleous & Harris, 2013; Sferruzzi-Perri et al., 2013; Smeekens et al., 2013; Wang & Ruan, 2013). In addition, oxygen availability can affect various signalling pathways controlling growth and development, as has been observed in the fruit fly D. melanogaster (Harrison & Haddad, 2011; Callier et al., 2013). Three other properties of growth strengthen my conclusion that growth is not merely dictated by metabolic rate. First, the widely different growth rates and final sizes of various organs in the body appear to be due to local regulatory mechanisms (Lui & Baron, 2011; Emlen et al., 2012), rather than being driven by tissue-specific metabolic rates. Contrary to the metabolic pacemaker view, in four different mammal species the mass-specific metabolic rate of various organs and tissues is not positively correlated with grown (adult) organ (or tissue) mass. Instead negative trends are shown, although only significantly so in humans (Fig. 3). The most massive tissues sampled (i.e. adipose and skeletal muscle tissues) that grow the most throughout the lifetime of



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Fig. 3. How mass-specific metabolic rate of various organs and tissues relates to their total grown mass in four mammal species. The metabolic rates are based on in vitro (A–C) or in vivo estimates (D). The data are for tissues or organs with relatively homogeneous tissues in laboratory mice (A, Martin & Fuhrman, 1955), laboratory rats (B, Field et al., 1939), dogs (C, Martin & Fuhrman, 1955) and humans (D, only male data are used, but female data are similar; see Elia, 1992; Wang et al., 2011). Note that tissues or organs that grow to relatively large masses (fat and skeletal muscle) tend to have relatively low metabolic rates, compared to those with smaller masses. The correlation coefficients between log10 mass-specific metabolic rate and log10 organ (or tissue) mass are all negative (A, r = − 0.247, N = 9, P = 0.521; B, r = − 0.272, N = 8, P = 0.514; C, r = − 0.200, N = 9, P = 0.607; D, r = − 0.936, N = 6, P = 0.006), rather than positive, as predicted by the metabolic pacemaker view. The tissues or organs are (from small to large mass): (A) spleen, heart, lungs, brain with same mass as kidneys and testes, liver, fat, and skeletal muscle; (B) spleen, heart, lungs, testes, kidneys, brain, liver, and skeletal muscle; (C) testes, spleen, kidneys, brain, heart, lungs, liver, fat, and skeletal muscle; (D) kidneys, heart, brain, liver, fat, and skeletal muscle.

many animals (Itazawa & Oikawa, 1986; Crandall, Hausman & Kral, 1997; Johnston, 1999) have unexpectedly low mass-specific metabolic rates. This observation is consistent with similar patterns observed in fish (Itazawa & Oikawa, 1986) and birds (Scott & Evans, 1992). As a result, low-energy tissues tend to increase in relative size during ontogeny, whereas high-energy tissues tend to decrease in relative size (Itazawa & Oikawa, 1986; Hsu et al., 2003). Small- to medium-sized, high-energy organs (e.g. kidneys, heart, liver and brain) engage in continually pressing, metabolically expensive, homeostatic activities (i.e. blood filtration and urine production, pumping of blood, detoxification and digestion, and responsive regulation of bodily functions, respectively). By contrast, relatively massive adipose and muscle tissues carry out intermittent activities (e.g. fat synthesis and body movements) between which the metabolic demand is relatively low. Furthermore, the low metabolic rates of adipose and muscle tissues are probably not a result of resource-supply limitation, because these tissues appear to have easy access

to resources carried by the circulatory system, due to their diffuse location throughout the body, and their high vascularity and demand-sensitive blood flow (Crandall et al., 1997; Weibel & Hoppeler, 2005). Second, in many animals early growth is chiefly the result of cell proliferation, whereas later growth mainly involves cell enlargement (Glazier, 2005; Lui & Baron, 2011). The metabolic pacemaker view offers no insight into these developmental changes in the cellular mode of growth. Conversely, however, these changes may have important effects on metabolic rate, in particular how it scales with body mass, as explained by the cell-size model of metabolic scaling (Davison, 1955; Kozłowski et al., 2003; Glazier, 2005; Glazier, Powell & Deptola, 2013). This model assumes that most metabolic activities involve energy transformations and (or) fluxes of ions and metabolites occurring in or across cellular membranes. Therefore, the scaling of metabolic rate with body mass should depend on how total cell surface area changes with growth. If growth occurs entirely by cell proliferation, then the scaling exponents (b) for both


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18 total cell surface area and metabolic rate should be 1, whereas if growth occurs entirely by cell enlargement, the b values should be 2/3. Intermediate b values should occur if growth occurs by a combination of cell proliferation and enlargement. As predicted, in many animals the metabolic scaling exponent is steeper during early than later development (see Glazier, 2005; Glazier et al., 2013, who discuss other lines of evidence supporting the cell-size model). Third, cell growth and proliferation is associated with not only an increase in the rate of metabolism, but also changes in the relative activity of specific pathways, including increased aerobic glycolysis and biosynthesis of macromolecules, but decreased oxidative phosphorylation (DeBerardinis et al., 2008; Vander Heiden et al., 2009; Agathocleous & Harris, 2013). This ‘metabolic shift’ promotes the availability of substrates needed for the production of increased cellular biomass (Aguilar & Fajas, 2010). Moreover, cell-cycle regulators appear to direct both the cell-cycle progression and the metabolic responses supporting it (Aguilar & Fajas, 2010; Duan & Pagano, 2011). Growth hormone also differentially stimulates carbohydrate, lipid and protein metabolism in intricate ways that vary among tissues and nutritional states (Leggatt et al., 2009; Møller & Jørgensen, 2009). These patterns and mechanisms suggest a complex co-regulation between growth and metabolism, rather than a simple metabolic pace-setting of growth. In Section IV.2, I pointed out that correlations between the rates and scaling of growth and metabolism may be explained in four ways. The first hypothesis that metabolic rate drives growth rate appears to be oversimplified and has little empirical support, although it is obvious that growth cannot occur without metabolic energy. The second hypothesis that growth rate drives metabolic rate is also oversimplified, but has more support than the first hypothesis. Growth may affect metabolic rate and how it scales with body mass in two fundamental ways: by its demand for energy and materials, and by its mode of operation as cell proliferation or enlargement. The third and fourth hypotheses may be the most promising of all. Growth and metabolism may be related to each other not only by reciprocal interactions (the third hypothesis), but also by being controlled in coordinated ways by genes, hormones and other regulatory factors (the third factor or set of factors of the fourth hypothesis). Further testing of these hypotheses should greatly increase our understanding of the mechanisms governing not only growth, but also more generally the pace of life.

VII. RECOMMENDATIONS FOR FURTHER RESEARCH I have argued that a complete understanding of the pace of life and its variation will depend on a balanced,

holistic consideration of mechanisms involving not just the acquisition and use of energy (resources), but also the acquisition and use of information (regulatory signals), two fundamental requirements of all life. Integrative studies of biological regulation show much promise for uniting these perspectives because a major ‘purpose’ of biological regulatory systems appears to be to use information (signals) about the external and internal environments of organisms to control their uptake and use of energy and nutrients in ways that maximize evolutionary fitness (Glazier, 2009c). For example, regulatory genes control the timing and tissue-specific expression of structural genes that code for enzymes and other molecules involved in metabolism, which in turn fuels various cellular and organismal activities (Adamafio, Okine & Adjimani, 2005). In addition, a major role of neuroendocrine systems is to regulate the acquisition and allocation of energy and other resources to various bodily functions (Bronson, 1989; Ricklefs & Wikelski, 2002). Focusing on both the proximate (functional) mechanisms of biological regulatory systems and their ultimate (evolutionary) causes (adaptive value) not only promises to unite the energetic and informational aspects of life, but also may help unify the two worlds of biology famously recognized by Ernst Mayr (1988), namely functional and evolutionary biology. Promising ways forward in achieving this unification include focusing on networks, which have already provided insights into not only how resources may be efficiently distributed in organisms (West et al., 1997; Banavar et al., 2010), but also how various metabolic and physiological processes may be interconnected (Jeong et al., 2000; Meyer et al., 2007; Sulpice et al., 2009; Stitt, Sulpice & Keurentjes, 2010; Bashan et al., 2011; Hofmeyr & Rohwer, 2011; Cohen et al., 2012; Kruger & Ratcliffe, 2012; Smeekens et al., 2013), and how information may be effectively transmitted in organized regulatory systems that control multiple metabolic, physiological, behavioural, and life-history activities (Ricklefs & Wikelski, 2002; Adamafio et al., 2005; Stitt et al., 2010; Cohen et al., 2012; Nijhout & Callier, 2013; Oyarzún & Stan, 2013). In particular, recent studies of metabolic networks have challenged the classical view that metabolic fluxes are driven solely by the supply of substrates (a major assumption of the MTE’s explanation for the body-size scaling of metabolic rate) by showing that demand (the rate of product formation and use) may also be important (Hofmeyr & Rohwer, 2011). Morandini (2013) has reviewed many examples of demand control of metabolic fluxes, which apparently prevents potentially harmful over-accumulation of metabolites. For example, the rate of production of adenosine triphosphate (ATP), a major energy currency of the cell, has been shown to be controlled by ATP demand (Koebmann et al., 2002; Clarke & Fraser, 2004; Ebert et al., 2011), as has the rate of photosynthesis by



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photosynthate demand (Paul & Foyer, 2001). The production of several other essential metabolic building blocks, such as NADPH, acetyl-coenzyme A, amino acids, lipids and sugars also appear to be regulated by demand (Morandini, 2013). This evidence provides biochemical support for my argument that the demand of various biological activities (e.g. growth, reproduction and locomotion) may dictate metabolic rates rather than vice versa, as expected by the metabolic pacemaker view (for further relevant discussion of supply–demand or source–sink interactions controlling plant metabolism and growth, see Stitt et al., 2010; Poorter, Anten & Marcelis, 2013; Sweetlove, Obata & Fernie, 2013). In short, metabolic rate may not only ‘push’ the rates of other biological processes, but also be ‘pulled’ by them. An additional promising way of advancing a holistic, mechanistic understanding of the pace of life is by exploring how specific nutrients, metabolites and enzymes can affect various signalling pathways (Buchakjian & Kornbluth, 2010; Gerosa & Sauer, 2011; Broach, 2012; Cai & Tu, 2012; Ward & Thompson, 2012; Agathocleous & Harris, 2013; Smeekens et al., 2013; Tiessen & Padilla-Chacon, 2013), including epigenetic control of gene expression (Morandini, 2013; Proveniers, 2013; Sassone-Corsi, 2013; You et al., 2013). Studies of this kind are expanding our awareness of the intimate, multi-directional interconnections between the energetic (metabolic) and informational (regulatory) sides of life. Another useful way forward may be to develop synthetic approaches that feature adaptive optimization of resource use, as predicted by various life-history models, in the context of specific energetic, allometric or other biophysical constraints (Rollo, 1995; Sibly, 2012). Two types of such synthetic approaches have already been proposed. One approach involves modelling the adaptive optimization of various life-history features in the context of biophysical constraints specified by the MTE (Berger et al., 2012; Bueno & López-Urrutia, 2012). A second approach views variation in the rates of metabolism and other biological processes, and their scaling with body size, as being the result of physiological responses or evolutionary adaptations occurring within the confines of physical boundary constraints specified by the metabolic-level boundaries hypothesis (Glazier, 2005, 2008, 2010). These constraints include volume-related limits on power production, and surface-area related limits on the fluxes of various resources and wastes between an organism and its environment. Other approaches are possible and should also be explored. To understand further how the various processes making up the pace of life are interrelated mechanistically, there is also a need to go beyond the present emphasis on merely testing for similarities between the scaling of metabolic rate and that of other biological rates.

Especially useful are experimental approaches, including artificial selection studies, natural experiments, and manipulations of specific processes (e.g. Needham, 1933; Tyler, 1942; Selman et al., 2001; Nilsson, 2002; Czarnołeski et al., 2008; Glazier et al., 2011), and multivariate statistical analyses that tease out the relative effects of body size, metabolic rate and other factors on the rates of various biological processes, including the detection of trait associations after correction for the effects of body size, temperature and (or) phylogenetic relatedness (e.g. McNab, 1980, 2002, 2012; Symonds, 1999, 2005; White & Seymour, 2004; Speakman, 2005; de Magalhães et al., 2007; Lanfear et al., 2007; Lovegrove, 2009). Both kinds of approaches already support my major conclusion that metabolic rate is probably not a universal pacemaker for other biological processes, which is consistent with the view of Peters (1983), a leading pioneer in the analysis of allometric scaling relationships, who argued that metabolism need not be the ‘master reaction’. As he remarked: ‘Organisms are interrelated systems in which all processes act in concert. Each allometric relation describes a process that is both cause and effect, master and servant, fundamental and derived’ (Peters, 1983, pp. 214–215). However, the extent to which various organismal systems act in concert is an open question. Hopefully, the above recommended experimental and comparative studies will reveal not only how the various rate processes making up the pace of life are interrelated, but also whether they are associated tightly and harmoniously (as highly coordinated syndromes aligned along a single slow–fast axis of variation) versus loosely and discordantly (as heterogeneous assemblages varying in multiple directions), or something in between (also see Bielby et al., 2007; Wiersma et al., 2007; Jeschke & Kokko, 2009; Versteegh et al., 2012; Blanco & Godfrey, 2013; Hille & Cooper, 2014; and Section I). Why there are associations (or not) among the rates of various biochemical, developmental, physiological, behavioural and life-history processes making up the pace of life is also an open question. Are rate processes coordinated by the integrating effects of metabolism (McNab, 1980, 2002, 2012; Western & Ssemakula, 1982; Glazier, 1985; Sibly et al., 2012, 2014; Wiersma et al., 2012; Pontzer et al., 2014), regulatory factors (Ricklefs & Wikelski, 2002; Parent et al., 2010; Swanson & Dantzer, 2014; see also Section VI), biological time, clocks or rhythms (Lindstedt & Calder, 1981; Prinzinger, 2005; Green, Takahashi & Bass, 2008; Bromage et al., 2012; Haydon et al., 2013; Neill, 2013), adaptive responses to mortality or demography (Promislow & Harvey, 1990; Oli, 2004; Ricklefs, 2010; Turbill, Bieber & Ruf, 2011), morphological constraints on nutrient uptake (Garratt et al., 2013), ecological constraints such as temperature (Crozier, 1924a; Brown et al., 2004; Dell et al., 2011; Parent & Tardieu, 2012; Wiersma et al., 2012), seasonality (Johansson et al., 2001; Blanco & Godfrey, 2013),


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20 food availability or quality (McNab, 1980, 2002; Glazier, 1985; Harshman, Hoffmann & Clark, 1999; Johansson et al., 2001; Mueller & Diamond, 2001; Krockenberger, 2003; Pontzer et al., 2010; von Merten & Siemers, 2012; Sibly et al., 2014) and the threat of predation (Johansson et al., 2001; Glazier et al., 2011; Handelsman et al., 2013), or some combination of the above (McNab, 1980, 2002; Glazier, 1985; Johansson et al., 2001; Brown et al., 2004; Parent et al., 2010; Bromage et al., 2012; Wiersma et al., 2012; Sibly et al., 2014)? Answers to these questions would not only be of fundamental theoretical importance, but also could have many practical applications in diverse fields of societal importance, including medicine, nutrition, pharmacology, gerontology, agriculture, forestry, fisheries, wildlife management, and environmental science.

VIII. CONCLUSIONS (1) Evidence discussed in this review indicates that metabolic rate is not a universal pacemaker for other biological processes. Metabolism may drive the operating speed of closely related physiological functions (e.g. breathing and heartbeat) and frequently cause temperature-related increases in the rates of various biological activities, as well. However, it also may be driven by or co-regulated with various biological processes (e.g. ontogenetic growth, food intake, and locomotor activity). In addition, the rates of some biological processes (e.g. ageing, circadian rhythms, and molecular evolution) appear to vary independently of metabolic rate. Furthermore, various intrinsic and extrinsic factors (e.g. genetic differences, hormonal action, and changes in nutrition and temperature) can cause a decoupling of the rates of metabolism, growth, development and other biological processes. (2) Modern advances in metabolic control theory (and associated empirical evidence) indicate that metabolic fluxes are governed not only by the supply of starting substrates, as traditionally believed, but also by the demand for metabolized end products. This dual supply and (or) demand control provides a biochemical basis for how metabolic rate may either ‘push’ the rates of other biological processes (by supplying various amounts of driving energy and materials), or be ‘pulled’ by them (by adaptively responding to their resource demand), or both. (3) Rates of metabolism, growth and other biological processes are not merely the passive result of energetic and physical constraints (as specified by the metabolic theory of ecology and other resource-centered models), but also are actively regulated by various genetic, cellular, and neuroendocrine signalling systems. (4) A comprehensive understanding of the pace of life, and its scaling with body size and temperature, will require knowledge about how both energy (resources)

and information (regulatory signals), two essential requirements for life, affect the rates of metabolism and other biological processes, and their multidirectional interconnections with one another and an organism’s internal and external environments. It will also require an appreciation that the scaling of various biological rates appears not to be the simple result of universal physical laws, but is ecologically sensitive and evolutionarily malleable (Glazier, 2005, 2010). This perspective is presented schematically as an Adaptable Informed Resource Use (AIRU) model, which may serve as a preliminary research guide. (5) There are major unresolved questions concerning the degree of harmony exhibited by the rate processes making up the pace of life. Are these processes highly integrated and synchronized or are they loosely connected and often discordant, or does an intermediate situation between these hypothetical extremes apply? Does the level of coordination of various organismal rate processes vary taxonomically and environmentally? What are the proximate (functional) mechanisms and ultimate (evolutionary) factors shaping the temporal structure of the pace of life? Exploring these fundamental questions promises to be an exciting area for further research that could yield many practical benefits.

IX. ACKNOWLEDGEMENTS I thank James H. Brown, John S. Terblanche and two anonymous reviewers for very helpful comments on earlier versions of this manuscript, and Juniata College for sabbatical leave support to visit Stellenbosch University where much of the writing was done.

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(Received 8 July 2013; revised 16 April 2014; accepted 17 April 2014 )


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