J Comp Physiol B (2013) 183:663–673 DOI 10.1007/s00360-012-0736-x
ORIGINAL PAPER
Performance correlates of resting metabolic rate in garden skinks Lampropholis delicata Lucy Merritt • Philip G. D. Matthews Craig R. White
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Received: 1 June 2011 / Revised: 15 November 2012 / Accepted: 4 December 2012 / Published online: 20 January 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Resting metabolic rates can vary greatly between individuals of the same species. These differences are generally repeatable and show moderate-to-high heritability, suggesting that they could be a target for natural selection. The present study therefore aimed to determine if inter-individual differences in resting metabolic rates (RMR) in garden skinks Lampropholis delicata were associated with inter-individual differences in a suite of physiological and behavioural variables: aerobic capacity, burst sprinting speed and thermal preference. Whole-animal measures of aerobic capacity and RMR were significantly positively correlated, but mass-independent measures were not. Burst sprinting speed and thermal preference were also not correlated with RMR. Keywords Thermal preference Aerobic capacity Sprint speed
Introduction Metabolism encompasses energy assimilation, expenditure and transformation within an organism, and metabolic rate is the pace at which these processes occur. Aerobic, field and resting metabolic rates (RMR) vary significantly between similarly sized individuals of the same species (Walton 1988; Bennett 1991; Berteaux et al. 1996; Noren 2002), exhibit significant repeatability (Berteaux et al.
Communicated by I.D. Hume. L. Merritt P. G. D. Matthews C. R. White (&) School of Biological Sciences, The University of Queensland, St Lucia, QLD 4072, Australia e-mail:
[email protected]
1996; Labocha et al. 2004; Terblanche et al. 2004; Nespolo and Franco 2007), and exhibit high genetic variance (Bennett 1991; Nespolo et al. 2005; Sadowska et al. 2005, 2009; Ronning et al. 2007; Nilsson et al. 2009; Schimpf et al. 2013). The consequences of inter-individual variation in metabolic rate for organismal performance have rarely been investigated. This is surprising, since inter-individual variation is the raw material on which natural selection acts (Arnold 1983; Cortes et al. 2009; Marras et al. 2010), and understanding the consequences of variation in metabolic rate amongst individuals could provide information on the selective pressures that contribute to evolution of metabolic rate. The present study therefore aims to determine if interindividual differences in RMR are correlated with interindividual differences in behavioural and physiological characteristics of the garden skink Lampropholis delicata. RMRs in ectotherms are usually defined as the energy expended whilst the animal is at rest, fasted and in a nonreproductive state (Andrews and Pough 1985; Christian et al. 1999; Sears 2005; Zhan et al. 2009). RMR represents approximately 13–63 % of daily energy turnover (Speakman 2000; Nagy 2005; White et al. 2006b). A negative correlation has been found between RMR and starvation resistance in rats (Rixon and Stevenson 1957), but not cockroaches Nauphoeta cinerea (Schimpf et al. 2012a), and in garden snails Helix aspersa RMR is under a combination of negative and stabilising selection such that individuals with low or intermediate levels of RMR are favoured over individuals with high RMR (Artacho and Nespolo 2009). Other studies have shown that RMR is negatively associated with gestation duration (Schimpf et al. 2012b) and offspring growth (Blackmer et al. 2005), is positively associated with over-winter survival in some species (Jackson et al. 2001), but not others (Boratyn´ski and Koteja 2009), and sometimes positively associated
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with other fitness-enhancing traits, such as aerobic capacity (AC) (Walton 1988; Hayes and Garland 1995; Wone et al. 2009) and social dominance (Metcalfe et al. 1995; Biro and Stamps 2010). The variation between traits and species in the strength and direction of associations with RMR (see e.g. Biro and Stamps 2010; White and Kearney 2013) has recently been hypothesised to underlie the magnitude of variation in RMR (Burton et al. 2011). In the present study, we add to this body of literature by testing for associations between RMR and a range of performance and behavioural traits, including thermal preference, burst sprinting speed and AC. These variables represent measures of energetically expensive and ecologically important activities that occur on a daily basis in the field, and provide critical information for our understanding of the consequences of inter-individual variation in RMR.
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1988; Hayes and Garland 1995; Wone et al. 2009). Mitochondrial processes, including proton leak and ATP production account for approximately 90 % of RMR (Rolfe and Brown 1997), and AC is principally determined by the mitochondrial and capillary volume of locomotory musculature (Weibel et al. 2004). Thus, individuals with high AC have more mitochondria than individuals with a low AC and, therefore, are predicted to also have a high RMR due to the overhead costs associated with maintaining these mitochondria. A negative relationship between burst sprinting performance and RMR is predicted, because a negative correlation between aerobic and anaerobic performance has been demonstrated in a range of species including the Atlantic cod Gadus morhua (Reidy et al. 2000), the Atlantic silverside Menidia menidia (Billerbeck et al. 2001) and the lacertid lizards Takydromus sexlineatus and Acanthodactylus pardalis (Vanhooydonck et al. 2001).
Physiological correlates of RMR: aerobic capacity and burst sprinting speed
Behavioural correlates of RMR: thermal preference
Aerobic capacity is the maximum rate of oxygen uptake of which an organism is capable (Owerkowicz and Baudinette 2008), representing the highest rate of metabolism that can be sustained aerobically. Natural selection has been shown to favour high and intermediate levels of AC in the lizard Lacerta vivipara (Le Galliard and Ferriere 2008). Sprint speed represents a correlate of maximal anaerobic capacity, and has been shown to be under positive directional selection in free-living adult garter snakes Thamnophis sirtalis fitchi (Jayne and Bennett 1990). Natural selection can, therefore, favour both high AC as well as high sprint speeds, at least in these species, and species that undergo high-intensity activities for extended durations tend to have high AC, whereas those that undergo vigorous activities for only short periods tend to have high sprint speeds (Vanhooydonck et al. 2000). For example, the helodermatid lizards Heloderma horridum and H. suspectum have high aerobic capacities that are advantageous during intensive male–male agonistic behaviours (Beck et al. 1995). Active, widely foraging species also have high AC, but sit-andwait predators have high anaerobic capacity (Bennett et al. 1984); some sit-and-wait predators also have low RMR (e.g. Lighton and Fielden 1995; Lighton et al. 2001). The lizard Cnemidophorus murinus has high endurance capacities, but low sprint speeds (Bennett and Gleeson 1979), and amongst Kalahari lacertid lizards, sit-and-wait predators have higher sprint speeds than active foragers (Huey et al. 1984). AC and sprint speeds differ in their level of importance to various ectotherms, depending on feeding and living habits. We hypothesise that RMR will be positively correlated with AC, as has been shown in other species (e.g. Walton
Thermal preference influences a wide range of temperature-dependent physiological traits in the laboratory and the field, including locomotor performance (Bauwens et al. 1995; Tattersall and Boutilier 1999; Ben-Ezra et al. 2008) and water and energy budgets (Wolf and Walsberg 1996). Thermal preference influences RMR due to the positive exponential relationship between body temperature and RMR (Novinger and Coon 2000; White and Seymour 2005). Behavioural thermoregulation is characteristic of reptiles (Wheeler 1986; Litzgus and Brooks 2000; Seebacher 2005), suggesting that individuals are capable of regulating RMR by regulating body temperature. Acclimation to low temperature, however, results in an increase in RMR, but no change in preferred body temperature (Wheeler 1986), suggesting that RMR and preferred body temperature are not causally related. A number of other studies have examined the effect of deviations from preferred body temperature on RMR and performance (e.g. Bartholomew et al. 1965; Bennett and John-Alder 1984; Angilletta 2001). Individuals with high RMR may select relatively cool temperatures to offset their relatively high energy requirements, for example, as has been observed in response to food restriction (Sogard and Olla 1996). We hypothesise that for animals acclimated to the same thermal conditions, RMR will be negatively correlated with preferred temperature, with lizards that have high an RMR selecting cooler environments than those with a low RMR and such a relationship could arise due to a trade-off between the performance advantages of maintaining a high body temperature and the energy cost of doing so. For example, microclimate selection has been shown to affect metabolic rate by as much as 50 % (Wolf and Walsberg
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1996). The overwintering frog Rana temporaria selects colder microclimates when experiencing hypoxic conditions to reduce metabolic rate and oxygen requirements (Tattersall and Boutilier 1997, 1999). Higher temperatures lead to increased AC in R. temporaria, however this depletes energy quickly (Tattersall and Boutilier 1999). To recover following strenuous activity, R. temporaria selects cooler environments to reduce recovery time (Tattersall and Boutilier 1999), an example of selecting colder temperatures to offset a necessary higher metabolism. Endotherms, such as the bat Macrotus californicus show a negative relationship between RMR and temperature at temperatures below the thermoneutral zone, and therefore behaviourally select warmer roosting sites in order to decrease energy expenditure (Bell et al. 1986).
Materials and methods
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Valley, QLD) supplemented with calcium and multi-vitamin powder (Aristopet Repti-Cal and Repti-Vite, Petstock, Indooroopilly, QLD) were provided every 3 days. Lizards were acclimated to laboratory conditions for at least 5 days prior to commencing experiments. Each lizard performed each experimental stage once and was given at least 24 h to recover between each stage. Lizards performed the stages in random order during the 12-h ‘daytime’ period. As L. delicata is diurnal, this reflects the most active stage during a 24-h cycle (Downes and Hoefer 2007). Performing each stage on each lizard was necessary to relate measures of behaviour and performance to measures of metabolic rate. As in similar studies, lizards were fasted for 2–4 days prior to measurement for each stage to ensure a post-absorptive state (Andrews and Pough 1985; Roe et al. 2005), as food present in the gut affects metabolism and hence thermoregulation (Niewiarowski and Waldschmidt 1992; Robert and Thompson 2000; Stapley 2006). Data were obtained for 55–57 individuals.
Study species, collection, and housing The garden skink L. delicata dwells in ground litter in a diverse array of habitats in Australia and New Zealand, predominantly in Eastern Australia (Howard et al. 2003). L. delicata is oviparous and heliothermic, with an average adult length of 40 mm (Thompson et al. 2001; Howard et al. 2003). L. delicata was chosen due to its local abundance and manageable size. This species is not under threat and is easily maintained in a laboratory, adapting quite easily to artificial housing (Burgin 1993). Lizards were collected from locations within the University of Queensland’s St Lucia campus, Brisbane, QLD, using fishing line baited with juvenile cockroaches N. cinerea and hand nets. Collection occurred during September and October 2009 to coincide with increased activity (Joss and Minard 1985; Burgin 1993) during the warmer months. Collection sites were separated by \600 m, so the pre-capture climatic experiences of individuals were similar. Coupled with the pre-measurement acclimation period, this limited the effect of phenotypic plasticity associated with thermal differences in the collection sites. Lizards were maintained individually at 25 ± 0.5 °C with a 12L: 12D h photoperiod in well-ventilated plastic boxes (28 9 20 9 14 cm) lined with a pine bark substrate. Boxes were arranged within a temperature-controlled room using random blocking placement around the space. Access to UVA/B lighting (ReptiGlo 2.0, Complete Pet and Vet, Yatala, QLD) was provided to the animals on a circular rotation. Heat cords for thermoregulation were provided between experiments. Water was provided ad libitum, and pinhead crickets Acheta domesticus and feeder cockroaches N. cinerea (Livefoods Unlimited, Tallebudgera
Experimental procedures Resting metabolic rate RMR (mW) was measured using positive pressure openflow indirect calorimetry at three temperatures (15, 25 and 35 °C) following established methods (Withers 2001; Lighton 2008). Lizards were placed individually in sealed respirometry chambers (94.25 cm3) within an incubator. Two chambers containing animals were used concurrently. The lizards were exposed to each of the three experimental temperatures in a random order. External air was scrubbed of water vapour and CO2 using Drierite (W. A. Hammond Drierite Company Ltd, Ohio, USA) and soda lime (Labchem, Ajax Finechem Pty Ltd, NSW, Australia), respectively, split into two gas streams and pumped through two mass flow controllers (Aalborg GFC17, Stanton Scientific, NSW, Australia) prior to entering the chambers. Air leaving the chambers was then scrubbed of water vapour using Drierite (W. A. Hammond Drierite Company Ltd, Ohio, USA), and the concentration of O2 exiting each chamber was measured using an Oxilla II oxygen analyser (Sable Systems, Nevada, USA). The flow controllers and gas analysers were interfaced with an A/D converter (PowerLab 16/30, ADInstruments, VIC, Australia) and data recorded at a sampling frequency of 4 Hz to a computer running LabChart 7 (ADInstruments, VIC, Australia). Flow rate through the chamber was 60 ml min-1; at this flow rate the mean difference in oxygen concentration between incurrent and excurrent air was *0.006 % for animals measured at 15 °C, which is within the range of O2 differences detectable by the analyser used in the present
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study (see e.g. Alton et al. 2007). RMR was measured for 1 h and 15 min. Data for the first 15 min were disregarded, as this time was used to allow the animal to settle in its new surroundings. The rate of oxygen consumption VO2 was calculated using standard equations (Withers 2001; Lighton 2008) and converted to RMR assuming a respiratory quotient of 0.8 and energy of equivalence of oxygen of 20.5 J mL-1 which limits the error in RMR to ±0.6 % when CO2 is not measured (Koteja 1996).
container was not rotated again until the lizard had righted itself to prevent injury to the animal. Each trial lasted at least 7 min (the equilibration time of the chamber) and rotations ceased once the lizard could no longer right itself, or after 10 min, whichever was shorter. Data for 1–2 min following equilibration were used for calculation of maximum rate of oxygen consumption.
Thermal preference
Burst sprinting speed (cm s-1) of the lizards was measured using an enclosed Perspex runway of approximately 100 cm length, lined with a rubber mat to provide traction. The runway was housed in a temperature-controlled room of 25 ± 0.5 °C, an approximation of average body temperature of lizards captured during activity in the field, and a temperature that facilitates proper physical performance (Burgin 1993). The lizard was placed in one end of the runway and prodded with a soft, foam-capped stick to encourage movement. Similar methods have been routinely used to measure burst sprinting speed in lizards (JohnAlder et al. 1986; Bauwens et al. 1995; Angilletta et al. 2002; Huyghe et al. 2005). Burst running speed to the other end of the runway was calculated using four light gates connected to an A/D converter (PowerLab 16/30, ADInstruments, VIC, Australia) and a computer running LabChart 7 (ADInstruments, VIC, Australia). Lizards were measured individually. Each lizard was run over 2 days, with four runs recorded on each day. The fastest single sprint for each lizard was used as a measure of burst sprinting speed.
Preferred body temperature (°C) of the lizards was determined in a laboratory environment using a thermal gradient, as practised in numerous studies (Van Damme et al. 1989; Ji et al. 1997; Zhang and Ji 2004). An enclosed Perspex runway of approximately 150 cm length and 14 cm width lined with sand was used. A temperature gradient was created using a combination of heat cords (URS, 80 and 50 W, Complete Pet and Vet, QLD, Australia) and tubes containing water chilled by a refrigerated circulating water bath (Julabo F-12, John Morris Scientific, QLD, Australia). An aluminium sheet was placed between the bottom of the runway and the tubes and cords to act as a conductive medium, evening out the distribution of hot and cold affecting the substrate. The gradient was used in a room temperature environment, as the temperature control devices used under the runway base ensured a stable ambient temperature. There was an approximately linear increase in temperatures along the gradient, with the lowest temperature at one end of the gradient approximately 15 °C and the highest temperature at the opposite end approximately 35 °C. Half of the tested individuals selected a temperature between 30 and 34 °C; only 7 % selected a temperature greater than 34 °C. Lizards were fasted for 2 days to ensure they were post-absorptive prior to measurement (see Andrews and Pough 1985; Roe et al. 2005). Lizards were observed individually. They were placed halfway down the gradient and allowed 1 h to voluntarily select their preferred temperature, during which time the gradient was covered to minimise stress. Body temperature was measured at the end of the hour using a calibrated noncontact infrared thermometer (FLUKE 572, RS Components, NSW, Australia). Aerobic capacity Aerobic capacity (mW) was measured as maximum rate of oxygen consumption during vigorous exercise. The measurement system was the same as for RMR, however, the container used to house the lizard was rotated, causing the lizard to fall on its back and regularly expend energy righting itself. Each time the lizard fell on to its back, the
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Burst sprinting speed
Additional experimental factors Body mass (g) was recorded at both commencement and cessation of experiments, and the time spent in captivity (days) was also recorded. Inclusion of these data in statistical analyses (see below) ensured that correlations between metabolic, performance and behavioural traits did not arise as a consequence of co-linearity with body size, or as a consequence of physiological or behavioural changes associated with captivity. Statistical analysis Standard simple and multiple regressions were used to analyse experimental data, as the explanatory variables of RMR, body mass, and time spent in captivity were continuous, as were the response variables of AC, thermal preference and burst sprinting speed. The variables AC, thermal preference and burst sprinting speed were plotted using linear regressions against RMR. A standard linear least squares regression was used to investigate the
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relationship between burst sprinting speed and AC. The representative values used for individual RMRs were calculated as RMR = e(25m?c), where m and c are the slope and intercept, respectively, of the relationship between ln(RMR) and temperature for each individual. The temperature dependence of RMR for each individual was then calculated as e10m, which represents the factorial change in RMR associated with a 10 °C change in temperature (Q10). Analyses are reported both for associations between whole-animal measures (e.g. between AC and RMR), and for analyses including body mass and time spent in captivity as covariates. The influence of potential high-leverage outliers was examined by excluding these points sequentially and repeating each analysis. This resulted in no significant change in any of the results, so all data were retained.
Results RMR and temperature RMR was significantly positively related to temperature (Fig. 1) and increased from 1.17 ± 0.10 (SEM) mW at 15 °C to 1.83 ± 0.13 and 2.89 ± 0.22 mW at 25 and 35 °C, respectively (n = 57 in all cases). Mean RMR calculated from measurements made at 15, 25 and 35 °C was 1.88 ± 0.10 mW at 25 °C. Mean RMR varied from 0.51 to 3.77 mW amongst individuals with a coefficient of variation (CV = standard deviation divided by mean) of 45 %. Mean RMR was significantly positively related to body mass (Fig. 2).
Fig. 1 Relationship between resting metabolic rate and temperature. Equation of the regression: log(metabolic rate) = 0.02 log(Temperature)-0.22. Means are shown as ±SEM
Fig. 2 Scaling of mean resting metabolic rate (filled diamonds) and aerobic capacity (unfilled squares) with body mass in L. delicata. The slopes for RMR and aerobic capacity are not significantly different (p = 0.72). Equation of the regressions: log(RMR) = 1.02 log(mass) ? 0.25 (solid line, r2 = 0.13); log(aerobic capacity) = 1.12 log(mass) ? 0.76 (dashed line, r2 = 0.24)
RMR and AC, burst sprinting speed and thermal preference Log-transformed AC was significantly related to log-transformed mean RMR: log(AC) = 0.74 ? 0.29 log(RMR) (r2 = 0.12, F1,52 = 6.8, p = 0.01, Fig. 3), but the association arose as a consequence of the shared association between both AC and RMR and body mass. Thus, when body mass is included in the model for AC, log-transformed AC is significantly related to body mass (F1,50 = 10.2, p = 0.002, Fig. 2), but not mean log-transformed RMR (F1,50 = 1.8, p = 0.18, Fig. 4) or time spent in captivity (F1,50 = 0.22, p = 0.51). Log-transformed burst sprinting speed was not significantly related to log-transformed mean RMR (F1,54 = 0.005, p = 0.94, Fig. 5). When log-transformed body mass and time in captivity were included as covariates the relationship remained non-significant (F1,52 = 0.016, p = 0.90) and the covariates themselves were not significant (log-transformed body mass: F1,52 = 0.0006, p = 0.98, time spent in captivity: F1,52 = 0.45, p = 0.51). Thermal preference was not significantly related to logtransformed mean RMR (F1,54 = 1.33, p = 0.25, Fig. 6). When log-transformed body mass and time in captivity were included as covariates the relationship remained nonsignificant (F1,52 = 0.81, p = 0.37, Fig. 6) and the covariates themselves were not significant (log-transformed body mass: F1,52 = 0.57, p = 0.45; time spent in captivity: F1,52 = 0.30, p = 0.59).
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Fig. 3 Relationship between log-transformed aerobic capacity (AC) and log-transformed mean metabolic rate (MR). log(AC) = 0.74 ? 0.29 log(RMR) (r2 = 0.12, F1,52 = 6.8, p = 0.01)
Fig. 4 Relationship between mean resting metabolic rate and aerobic capacity in L. delicata. Mean resting metabolic rate is not significantly related to aerobic capacity (r2 = 0.05, p = 0.18). Mean resting metabolic rate and aerobic capacity are both significantly related to body mass (see ‘‘Results’’), so residuals are presented
Aerobic capacity and burst sprinting speed
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Fig. 5 Relationship between mean resting metabolic rate and burst sprinting speed in L. delicata. RMR is not significantly related to burst sprinting speed (r2 = 0.0001, p = 0.62). Mean resting metabolic rate is significantly related to body mass (see ‘‘Results’’), so residuals are presented
Fig. 6 Relationship between mean resting metabolic rate and thermal preference in L. delicata. RMR is not significantly related to thermal preference (r2 = 0.01, p = 0.19). Mean resting metabolic rate is significantly related to body mass (see ‘‘Results’’), so residuals are presented
Aerobic capacity was not significantly related to burst sprinting speed (F1,53 = 1.02, p = 0.31, Fig. 7). The relationship remained non-significant when log-transformed body mass and time spent in captivity were included as covariates (F1,51 = 0.95, p = 0.33); the covariate log-transformed body mass was significant (F1,51 = 16.6, p = 0.0002), but time spent in captivity was not (F1,51 = 0.54, p = 0.47).
differ to that of RMR (log-transformed mass by activitytype interaction: F1,107 = 0.18, p = 0.68). The scaling exponents of RMR and aerobic capacity were 1.02 and 1.12, respectively.
Scaling RMR and aerobic capacity with body mass
Metabolic rate is one of the most widely measured physiological traits. In the present study, RMR varied by up to sevenfold amongst individuals, with a coefficient of variation of 45 %. This degree of inter-individual variation is
RMR and AC both increased with body mass (Fig. 2). The scaling exponent of aerobic capacity did not significantly
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Discussion
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comparable to that reported in other studies (see Sieg et al. 2009; Marras et al. 2010), and such variation has been shown to exhibit significant repeatability (Berteaux et al. 1996; Labocha et al. 2004; Terblanche et al. 2004; Nespolo and Franco 2007) and moderate-to-high heritability (Bennett 1991; Ronning et al. 2007; Nilsson et al. 2009; Swallow et al. 2009; White and Kearney 2013; Schimpf et al. 2013). In garden skinks, RMR is positively exponentially related to temperature (Fig. 1) and body mass (Fig. 2), but varies considerably between individuals. That the RMR of individuals of L. delicata and many other species varies so much suggests that the variation might be important. In the present study, we set out to investigate the consequences of this inter-individual variation in RMR for the behaviour and performance of garden skinks by relating RMR to measures of aerobic performance (aerobic capacity), anaerobic performance (burst sprinting speed) and behaviour (thermal preference).
RMR and aerobic capacity RMR is significantly positively related to aerobic capacity (Fig. 3), but the association is not significant once their shared association with body mass is accounted for (Fig. 4). A relationship between RMR and aerobic capacity is predicted because high aerobic capacity is associated with relatively large hearts and lungs (Hochachka and Burelle 2004), and high densities of muscle mitochondria (Weibel et al. 2004; Clarke and Po¨rtner 2010), and because these contribute significantly to energy turnover in resting conditions (Rolfe and Brown 1997; Hulbert and Else 2000). It has been postulated that the high RMR associated with endothermy arose as a consequence of a correlated response to selection for high aerobic capacity (‘the aerobic capacity model for the evolution of endothermy’: Bennett and Ruben 1979; Hayes and Garland 1995; Frappell and Butler 2004). The results of the present study offer only weak support this hypothesis, because although aerobic capacity and RMR are positively associated in L. delicata (Fig. 7), the association arises because each is positively associated with body mass (Fig. 2). If their shared association with body mass is accounted for, aerobic capacity and RMR are not related (Fig. 4). Whilst a number of phenotypic studies of reptiles and amphibians have reported significant positive associations between aerobic capacity and RMR, such relationships are not ubiquitous (Hayes and Garland 1995; Gomes et al. 2004) and a number of alternative hypotheses for the evolution of endothermy have been proposed (Farmer 2000; Koteja 2000; Grigg et al. 2004; Clarke and Po¨rtner 2010; Lovegrove 2011). With respect to the aerobic capacity model, there is a clear need to test for genetic associations between aerobic capacity
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and RMR (see e.g. Wone et al. 2009; Clarke and Po¨rtner 2010; Hayes 2010; Nespolo et al. 2011). Aerobic capacity in L. delicata is significantly positively related to body mass. Aerobic capacity, or maximum metabolic rate (MMR), has been found to have a positive allometric relationship with body mass in a number of studies. MMR has been found to scale allometrically with body mass in a number of endotherms (see Taylor et al. 1981; White and Seymour 2005) and ectotherms, including marine turtles Caretta caretta, Eretmochelys imbricata and Natator depressus, whose aerobic dive limits are allometrically related to body weight (Hochscheid et al. 2007), and the helodermatid lizards H. horridum and H. suspectum (Beck et al. 1995). Typically, the scaling exponent of MMR is higher than that of RMR (White et al. 2007, 2008), however this generalisation does not apply to garden skinks. The scaling exponent of aerobic capacity scaling did not significantly differ to the exponent of RMR (Fig. 2). Mean aerobic scope (=aerobic capacity dived by RMR) was 4.6, which is lower than the mean value observed in other reptiles (7.2-fold at 20 °C; 8.2-fold at 30 °C) (Bennett 1982), although data are available for no species smaller than 10 g. The aerobic scope of small endotherms is typically lower than that of large ones (Bishop 1999; Bundle et al. 1999), and several species of small mammal have reported aerobic scopes of between 2.5- and 5-fold (Ruben and Battalia 1979; Koteja 1987), although most species are higher (e.g. Hinds et al. 1993; White et al. 2006a). The low apparent aerobic scope of L. delicata could therefore be genuine, arising perhaps because of their small size, or it could be an artefact of the experimental conditions. For example, in the present study, aerobic scope was calculated using measurements of RMR obtained during the active circadian phase rather than during the inactive phase when RMR could be lower. Alternatively, measures of metabolic rate during righting may not be representative of aerobic capacity, and future work using this method should include measurements of blood lactate concentration to more definitively establish that animals are working above their aerobic capacity. Metabolic rate could be measured during treadmill locomotion at a range of speeds (e.g. Dohm et al. 1998; Weinstein and Full 1999), although this technique was deemed inappropriate for L. delicata because they readily autotomise their tails (Alibardi 1995). RMR and burst sprinting speed Burst sprinting speed was not significantly related to RMR (Fig. 5), although the degree of scatter in the data limit strong conclusions regarding this relationship with the sample size employed in the present study. Aerobic
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maximise growth rates (Porter and Tschinkel 1993). It is possible, however, that the lack of a relationship in the present study could arise because thermal preference has low repeatability, as has been found in multiple Cordylid lizard species (Clusella-Trullas et al. 2007). Conclusion
Fig. 7 Relationship between burst sprinting speed and aerobic capacity in L. delicata. Burst sprinting speed is not significantly related to aerobic capacity (r2 = 0.02, p = 0.27). Aerobic capacity is significantly related to body mass (see ‘‘Results’’), so residuals are presented
performance (aerobic capacity) was not significantly related to anaerobic performance (burst sprinting speed) (Fig. 7). These results are not in general accordance with the published literature investigating these variables (e.g. Garland 1988; Reidy et al. 2000; Moon and Tullis 2006). These studies have reported a positive relationship between speed and endurance in garter snakes T. sirtalis (Garland 1988) and a negative relationship between aerobic and anaerobic swimming performance in Atlantic cod G. morhua (Reidy et al. 2000). The present study supports previous work on whiptail lizards Cnemidophorus tigris punctilinealis and Cnemidophorus tigris marmoratus, which also demonstrated a non-significant relationship between SMR and sprint speed (Dohm et al. 1998). RMR and thermal preference RMR was not significantly related to thermal preference (Fig. 6). We hypothesised that individuals with a higher RMR would select cooler environments and those with a lower RMR would select warmer environments to offset energy maintenance costs, but this hypothesis was not supported. Therefore, it is likely that physiological factors other than RMR are the primary determinants of microclimate selection. For example, food location and digestion have been shown to influence thermal preference in a range of ectotherms (Porter and Tschinkel 1993; Sogard and Olla 1996; Lichtenbelt et al. 1997; Secor 2009). The field body temperature of the herbivorous green iguana species Iguana iguana is determined by food digestion and intake (Lichtenbelt et al. 1997), and fire ants Solenopsis invicta select temperatures that minimise metabolic costs and
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The present study supports the hypothesis that inter-individual variation in RMR is significantly correlated with inter-individual variation in other physiological variables, but the only significant association identified was mediated by shared associations between body mass and both aerobic capacity and RMR. Thus, unsurprisingly, large individuals have high aerobic capacities and high RMR (Figs. 2, 3). For animals of a given body size, however, aerobic capacity and RMR are not related (Fig. 4). Burst sprinting speed and thermal preference were also not significantly related to RMR (Figs. 5, 6, respectively). The lack of significant associations between measurements of RMR and other traits raises the question of why RMR varies so much (up to several-fold) between individuals. Whilst no associations between mass-independent measures of RMR and other traits were identified in the present study, many other studies have identified significant associations, although they are certainly not ubiquitous (reviewed by Biro and Stamps 2010; Burton et al. 2011; White and Kearney 2013). Burton et al. (2011) note that whilst RMR is likely linked with fitness, the strength and direction of association is context dependent, and modulated by environmental conditions. Spatial and temporal variation in these environmental conditions might, therefore, act to maintain variation in RMR, and could explain why the association between RMR and other traits varies between species and contexts. It is also possible that phenotypic studies, such as the present one, fail to identify genetic associations between the traits of interest. For example, Sadowska et al. (2009) identified significant genetic correlations between basal metabolic rate and both growth rate and the ability to cope with a low-quality herbivorous diet in bank voles Myodes (Clethrionomys) glareolus. Phenotypic analyses failed to identify both associations. Future work attempting to understand the causes and consequences of variation in RMR, and the diversity of previously examined associations, should therefore consider both the context in which phenotypic measurements are made and the genetic architecture of the traits of interest (Burton et al. 2011; Nespolo et al. 2011; Konarzewski and Ksia˛z_ ek 2013). Acknowledgments Cameron Schofield, Natalie Schimpf, Daniel Hancox, Benjamin Barth, Candice Bywater and Skye Cameron provided help with animal capture; Roberto Nespolo and an anonymous
J Comp Physiol B (2013) 183:663–673 reviewer provided comments that helped us improve an earlier version of the manuscript. This research was supported by the Australian Research Council (projects DP0879605 and DP0987626).
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