Blood flow in guinea fowl Numida meleagris as an indicator of energy expenditure by individual muscles during walking and running
1 Department of Biology, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA
Abstract
Running and walking are mechanically complex activities. Leg muscles must exert forces to support weight and provide stability, do work to accelerate the limbs and body centre of mass, and absorb work to act as brakes. Current understanding of energy use during legged locomotion has been limited by the lack of measurements of energy use by individual muscles. Our study is based on the correlation between blood flow and aerobic energy expenditure in active skeletal muscle during locomotion. This correlation is strongly supported by the available evidence concerning control of blood flow to active muscle, and the relationship between blood flow and the rate of muscle oxygen consumption. We used injectable microspheres to measure the blood flow to the hind-limb muscles, and other body tissues, in guinea fowl (Numida meleagris) at rest, and across a range of walking and running speeds. Combined with data concerning the various mechanical functions of the leg muscles, this approach has enabled the first direct estimates of the energetic costs of some of these functions. Cardiac output increased from 350 ml min–1 at rest, to 1700 ml min–1 at a running speed (2.6 m s–1) eliciting a of 90% of . The increase in cardiac output was achieved via approximately equal factorial increases in heart rate and stroke volume. Approximately 90% of the increased cardiac output was directed to the active muscles of the hind limbs, without redistribution of blood flow from the viscera. Values of mass-specific blood flow to the ventricles, 15 ml min–1 g–1, and one of the hind-limb muscles, 9 ml min–1 g–1, were the highest yet recorded for blood flow to active muscle. The patterns of increasing blood flow with increasing speed varied greatly among different muscles. The increases in flow correlated with the likely fibre type distribution of the muscles. Muscles expected to have many high-oxidative fibres preferentially increased flow at low exercise intensities. We estimated substantial energetic costs associated with swinging the limbs, co-contraction to stabilize the knee and work production by the hind-limb muscles. Our data provide a basis for evaluating hypotheses relating the mechanics and energetics of legged locomotion.
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Introduction
Linking mechanics and energetics has been a major goal of integrative studies of locomotion, but this goal has remained elusive for terrestrial legged locomotion. The diversity of function of the limb muscles used in legged locomotion has been highlighted in studies that measure mechanical function of active muscles using sonomicrometry and electromyography. Some muscles have been found to operate with little length change during force production, for example, the lateral gastrocnemius in running turkeys (Roberts et al. 1997) and the semimembranosus in running dogs (Gregersen et al. 1998). Other muscles have been shown to actively shorten and perform work, e.g. the long-head of the triceps brachii in running dogs (Gregersen et al. 1998), and the fibularis longus in running turkeys (Gabaldon et al. 2004). Still other muscles are lengthened while active, acting as brakes to absorb work, e.g. the iliofibularis muscle in running guinea fowl (Buchanan, 1999; Marsh, 1999). Muscles have also been found to perform different mechanical functions in different gaits, or between contralateral limbs in an asymmetrical gait (Gillis & Biewener, 2001). Unfortunately, the diversity of mechanical function being revealed in these studies does not lend itself to any simple conclusions about the links between mechanical function and energy use. To establish such links, the mechanical function of individual muscles needs to be connected to their energy use. In other words, we need to measure the increase in energy use by individual muscles during running.
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We have recently suggested that blood flow to individual active muscles as measured with injected microspheres may represent the most reliable alternative for indicating their energy use during exercise (Marsh et al. 2004). Strong evidence is available indicating that the rate of blood flow to an active muscle provides a reliable indication of its aerobic energy expenditure. The increase in local vascular conductance associated with skeletal muscle activity is under the control of a number of complexly interacting factors, primarily local in origin (reviewed by Koller & Kaley, 1991; Segal, 1994; Radegran & Hellsten, 2000; Laughlin & Korzick, 2001; Ellsworth, 2004). These local mechanisms lead to an increase in flow in response to changing exercise intensity that is compatible with the known recruitment order of fibre types (Laughlin & Armstrong, 1982; Armstrong & Laughlin, 1983, 1985; Manohar, 1986; Armstrong et al. 1987), and is proportional to the increase in metabolic rate (Delp & Laughlin, 1998; Murrant & Sarelius, 2000; Laughlin & Korzick, 2001; Boushel, 2003).
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We have used blood flow measured with coloured microspheres as our proxy measure for energy use by the skeletal muscles used in walking and running. Microsphere measures of blood flow have been validated extensively and used to measure muscle blood flow in a number of studies that focused mainly on mass-specific blood flow and its control in relation to such parameters as fibre type and training regime (Armstrong, 1988). Microsphere blood-flow measurements have only recently been used to estimate the total energy use by all the individual muscles used in locomotion (Marsh et al. 2004).
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The aim of the present study was to measure how blood flow, and by inference, energy expenditure, was altered in all the muscles of the avian hind limb with increasing exercise intensity during walking and running. Improving our understanding of muscle energy use during legged locomotion will have important implications in several areas. First, we can begin to quantify the energetic cost of the diverse mechanical functions served by the various muscles activated during walking and running (Marsh et al. 2004). Second, better knowledge of distribution of energy use will allow the evaluation of inverse-dynamics calculations that attempt to relate externally measured forces and movements to underlying muscle function. Third, understanding the distribution of energy use allows the evaluation of hypotheses that attempt to explain the patterns of energy use seen with varying speed and body size (Marsh et al. 2004).
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We measured blood flow during treadmill exercise in helmeted guinea fowl (Numida meleagris). In the wild, members of this species mostly walk and run as part of their daily activities and fly only to escape predation (Ayeni, 1982). Domesticated strains of guinea fowl have retained a considerable running capacity and are capable treadmill runners, having been used in several laboratory investigations of running (Fedak et al. 1974; Roberts et al. 1998; Gatesy, 1999a,b; Buchanan & Marsh, 2001; Daley & Biewener, 2003). Some of the data presented here have previously been used to determine the relative division of energy expenditure between stance and swing phases of the stride (Marsh et al. 2004). In the present study, a more complete description of how absolute blood flow to individual muscles changes with running speed is given. Cardiac performance data, and blood flows to tissues and organs other than the leg muscles are also presented. Additionally, the data provide a baseline for comparing data on muscle blood-flow experiments in which locomotor function is altered, for example by load carrying or uphill running.
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Methods
Animals and training protocol
Guinea fowl (Numida meleagris) were obtained from The Guinea Farm (New Vienna, IA, USA) as hatchlings and cage reared at the Northeastern University Division of Laboratory Medicine. At the time of the measurements, the birds were between 10 and 14 months old. Birds had ad libitum access to food and water and were maintained on a 12 : 12 h light dark cycle. Mean body mass was 1.48 ± 0.06 kg (± S.E.M., n = 5, range 1.32–1.68 kg, 3 female, 2 male). The Northeastern University Institutional Animal Care and Use Committee approved all procedures involving live animals.
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For 2 months prior to measurements of and blood flow, we trained the birds to run on a motorized treadmill (Trimline 2600, Trimline, USA, 120 cm x 44 cm tread area). During training and subsequent experiments, the guinea fowl ran inside a three-sided box with a partial top. The box was open at the back and had a mirror mounted on the front facing the running bird. A duct brought cool air from an air conditioner to one side of the box. Airflow was controlled with a slide such that sufficient cool air was allowed to flow into the box to prevent the birds from continuously panting. The training regime consisted of running for approximately 30 min per day, 5 days a week at speeds ranging from 1.5 to 3.3 m s–1. At the end of the training period all birds could sustain 30 min of exercise at 2.5 m s–1.
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Oxygen consumption during running
We measured the rate of oxygen consumption at rest and at a range of running speeds in each bird. This procedure allowed us to measure the individual maximum aerobic capacity of the birds. Subsequently, this allowed us to run the birds at known proportions of during blood-flow measurements. Respiratory gases were collected using a flow-through respirometry system with the birds wearing a loose-fitting mask. Details of the respirometry equipment and procedures are the same as those used by Ellerby et al. (2003) and Marsh et al. (2004).
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Surgical procedures prior to the blood-flow measurements
We anaesthetized the bird using isoflurane and maintained body temperature using a heat mat placed under the bird. Using standard surgical techniques, the brachial arteries were sequentially isolated and cannulated using custom-made polyurethane, saline-filled cannulae. The cannula in the right brachial artery was advanced until its tip was in the left ventricle, as indicated by recording blood pressure. The ventricular cannula was subsequently used to inject microspheres. The cannula in the left brachial artery was advanced to a depth such that the tip of the cannula was located in the brachiocephalic artery. The cannulae were secured in the vessels and the incisions closed with standard surgical techniques. The plugged cannulae were sutured to the skin near the elbow and left coiled under a covering of Vetrap (3M). Blood-flow measurements were carried out on the day after the surgery.
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Microsphere injections
Blood-flow measurements were made using 15 μm diameter injectable polystyrene microspheres (Triton Dye-Trak VII+, Triton Technologies, CA, USA). The microspheres were supplied suspended in sterile physiological saline containing 0.05% Tween-80 and 0.01% thimerosal at a density of 3 million per ml. For each batch of spheres of each colour, we determined a standard curve of absorption versus wavelength (325–650 nm) based on the dye extracted from a known mass of the well-mixed suspension of spheres. Microsphere suspensions were vortexed for 45 s prior to being drawn up into the injection syringe to ensure even suspension of the spheres. Syringes were weighed to the nearest milligram before and after filling to give a precise measure of the amount of microsphere solution in the syringe. The amount of spheres actually injected was determined from the amount in the syringe minus the residual spheres remaining in the syringe tip and the injection stopcock. Approximately 1 x 106 spheres were injected at each level of exercise.
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Microsphere injections were made at four levels of metabolic effort: (1) at rest in a darkened box; (2) treadmill exercise at 0.5 m s–1 (a walking gait); (3) treadmill exercise at 1.5 m s–1 (a readily sustainable running speed); and (4) treadmill exercise at a speed at which was approximately 90% of (2.5–2.78 m s–1). Prior to determining resting blood flow the bird was left in a darkened box for 10 min, sufficient time for heart rate and (based on separate measurements) to stabilize. In the box the birds sat quietly with their legs folded under themselves. After being removed from the box the birds walked for at least 2 min at 0.5 m s–1 before the experimental sequence of runs was initiated. Following this initial run the sequence of speeds for microsphere injections was: (1) high-speed running, (2) 1.5 m s–1, and (3) 0.5 m s–1. Between each test speed the bird walked for 2 min at 0.5 m s–1. The bird maintained each test speed for 2–3 min prior to the injection of microspheres. This time period was well beyond the time required for and heart rate to stabilize.
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The reference blood withdrawal was made into a heparinized syringe at a rate of between 1.0 and 1.75 ml min–1 using a syringe pump (Genie YA-12, Kent Scientific, CT, USA). Higher withdrawal rates were used at the higher levels of exercise, during which elevated cardiac output results in greater dilution of the spheres in the blood withdrawal. Approximately 10 s after starting the blood withdrawal, the microspheres were injected via the ventricular cannula. The injections were made through a Luer port of a sterile 3-way stopcock. A Millar SPR-407 pressure transducer was connected to a second Luer port to measure ventricular pressure at all times except during the injections. The microspheres were introduced as a bolus over a 20-s period. The cannula was immediately flushed with 0.7 ml of sterile saline to ensure that all the microspheres had been injected into the ventricular blood. The reference withdrawal was continued for sufficient time after completing the flush to clear all blood containing microspheres from the withdrawal cannula (30 s). The total amount of blood withdrawn was typically 1.2–1.6 ml. Residual spheres were visible in the injection syringes and injection stopcocks. New stopcocks were used for each injection to prevent cross contamination of microsphere colours. We rinsed the stopcocks and injection syringes with 100% ethanol into 50 ml centrifuge tubes to recover residual spheres for analysis.
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Blood haemoglobin and lactate concentrations
After completion of the reference withdrawal, a further 120 μl of blood was taken from the withdrawal cannula to determine blood haemoglobin and lactate concentrations. Haemoglobin concentration was measured using a Drabkin's reagent kit (Sigma Chemical Company, 525A). Lactate concentration was measured using an enzymatic UV kit (Sigma Chemical Company, 826B).
Microsphere recovery
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After completion of microsphere injections the animals were killed by an overdose of pentobarbital solution. All the muscles from one hind limb were dissected and weighed. Selected muscles from the contralateral limb were also taken as a check that the microspheres were adequately mixed in the ventricle and distributed symmetrically in the systemic circulation. We also removed and weighed all the major abdominal and thoracic organs, the brain, the flight muscles, and portions of the inspiratory and expiratory muscles. The entire mass of each hind-limb muscle was processed. Muscles larger than 10 g were subdivided between several tubes. The large pectoralis and supracoracoideus muscles were subsampled from three locations and the total mass recorded to enable calculation of total flow to the muscle. The whole mass of the brain, thoracic and abdominal organs were processed except for the liver and gizzard, which were sub-sampled, and the total flow calculated from the mass-specific flow and the total mass of the organ.
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Prior to any further processing a known amount of microspheres of a colour (navy) not injected into the animal was placed in each tube with the tissue or blood samples. This additional colour acted as a control to correct for the loss of microspheres during processing (Chien et al. 1995). Final sphere amounts were referenced to the mean number of navy spheres recovered from four unprocessed tubes containing navy spheres and scaled accordingly. Typically 70–90% of the microspheres were successfully recovered from the processed tissue samples.
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The microspheres were recovered using a modified version of the protocol recommended by Triton Technologies (see online supplement to Marsh et al. 2004). Following extraction, we weighed the tubes containing the dry microsphere pellets, added cellosolve acetate, and re-weighed the tubes to precisely determine the solvent volume. The spheres were resuspended with a vortex mixer to ensure complete dye extraction. After centrifugation to pellet out any debris the absorbance spectra of the dye mixtures were measured using a scanning spectrophotometer (Ultrospec 3300pro, Amersham, USA). Samples were scanned across a range of wavelengths from 350 to 725 nm. The numbers of microspheres of the four experimental colours and the navy process control were calculated from absorbances measured at six wavelengths representing the peak absorbances of each colour in the sample and the peak absorbance of a rare low-wavelength contaminant. Calculations were done using a matrix inversion technique implemented in Microsoft Excel. The Excel implementation was a modified version of one supplied by Triton Technology.
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Calculation of blood flow
The number of microspheres in the reference blood withdrawal was used to calculate the mass-specific tissue flow rate in ml g–1 min–1 according to:
where is the reference blood withdrawal rate in ml min–1, Nt is the number of spheres in the tissue sample, Nb is the number of spheres in the reference blood sample, and Mt is the mass of the tissue. The number of spheres collected in the withdrawal sample can also be used to calculate cardiac output as follows:
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where Ni is the number of spheres injected.
Validation of microsphere measures of blood flow in guinea fowl
We injected the microspheres into the left ventricle, a technique that has been shown to provide adequate mixing in mammals (Buckberg et al. 1971). To assess mixing, we measured the flow to several of the leg muscles bilaterally in most birds. On average, the flow to the muscles on both sides was equal. The slope of the regression of right-side muscle blood flow versus left-side total muscle blood flow was 0.98 ± 0.04 (slope ± 95% confidence interval (C.I.), n = 40). Using flow to the kidneys to assess bilateral flow, as has been done in some mammalian studies, is potentially problematical in birds because of the complex nature of the renal circulation with possibly variable amounts of renal-portal flow due to valving (Odlind, 1978), and the possible capture of microspheres entering the renal portal circulation after passing through arterio-venous shunts in the legs (Wolfenson, 1983). Even after correcting for differences in kidney mass, our measurements of arterial flow to the kidneys showed somewhat greater variability in bilateral distribution as compared with the values for the leg muscles, and a slight bias toward greater flow to the left kidney. The slope of the regression of right-side kidney blood flow versus left-side kidney blood flow (mass-specific) was 0.93 ± 0.06 (slope ± 95% C.I., n = 20).
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Our data indicate that in guinea fowl most of the microspheres were trapped in the first pass through the systemic circulation, as was found by Boelkins et al. (1973) for 15 μm spheres in the domestic fowl (Gallus domesticus). In the present study a mean of 4.2 ± 1.0% of the injected spheres were recovered from the lungs. The highest percentage of injected spheres appearing in the lungs occurred at rest with lower amounts at the higher speeds. The data of Wolfenson (1983) on the domestic fowl suggest that most of the spheres lodging in the lungs derive from systemic flow that passes through arterio-venous anastomoses in the skin in which 15 μm spheres are not trapped. The avian lung is known to trap 15 μm spheres completely (Odlind, 1978). After correcting for the spheres recovered in the lungs, we were able to account for all of the cardiac output except a mean of 140 ± 23 ml min–1 across all exercise levels. This flow presumably represents the flow to the parts of the body not analysed in this study. The percentage of the cardiac output accounted for increased with speed and reached a mean value of 95% at the highest speed.
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Under our experimental conditions, the microsphere injections were unlikely to have impaired normal function of the cardiovascular system. Heart rate and blood pressure were not influenced by the injections and the number of muscle capillaries blocked was small. For example, the mean total number of spheres of all colours accumulated per gram of gastrocnemius muscle was 4300. Assuming 1-mm long capillaries and a capillary density similar to that found in related galliform birds (Snyder, 1990), we estimate that approximately 0.5% of the total capillaries were blocked in our experiments.
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We verified the accuracy of our matrix inversion calculations and the adequacy of our extraction procedures by measuring known mixtures of spheres of different colours either alone or with control muscle samples. Previous work has indicated that at least 400 spheres must be recovered in a tissue sample to restrict errors in measured flow to less than 10% (Buckberg et al. 1971). In the present study we recovered at least 400 spheres of all colours in all tissue samples except for the spheres injected at rest for three of the smallest muscles, the CFC, CFP and the AMB.
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Statistical analyses
Analysis of variance was conducted using the general linear model in the application SPSS (versions 10 and 11 for the Macintosh) to test for significant differences in blood flow between different levels of exercise. Significant variation existed in the level of flow among animals. Consequently, an animal identifier was entered as a factor in the model along with the exercise level. This model allowed us to evaluate consistent patterns of changing flow with exercise level after removing the variation among the individual animals. To assess whether significant increases in flow occurred with increasing exercise intensity, we examined planned contrasts between adjacent levels of exercise (rest to 0.5 m s–1; 0.5–1.5 m s–1 and 1.5 to > 2.4 m s–1). As the contrasts procedure assumes equal variances, which was not the case for all tissues tested, we examined cases of borderline significance using the Games-Howell post hoc comparison, which does not assume equal variances.
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To examine the changes in fractional contribution of the individual muscles to the increases in flow between exercise levels, we transformed the fractional values with an arcsine transformation. The transformed value is equal to the arcsine of the square root of the fractional value (Sokal & Rohlf, 1981). Before the transformation, negative values (decreases in flow), were coded as zero. This coding is equivalent to assuming that no decreases in flow occurred with increasing exercise level. This assumption is reasonable for our data because no muscle showed a statistically significant decrease in flow with increasing exercise intensity. ANOVA on the transformed variables was undertaken with only the change in exercise level as a factor, because no significant variation in the transformed values existed among animals.
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Results
Individual tissues tested and their masses
Oxygen consumption
Figure 1 shows the relationship between oxygen consumption and running speed for two individuals. At speeds less than the maximum aerobic speed a linear relationship between and speed was observed. In three birds, the slope of versus speed remained constant up to (Fig. 1A). In two birds, the slope decreased slightly at speeds near the maximum aerobic speed (Fig. 1B). The mean net cost of transport for the birds was of 8.9 J kg–1 m–1, which is similar to that previously measured for running guinea fowl (Ellerby et al. 2003). The mean was 149.7 ± 3.0 ml min–1. The mean levels of oxygen consumption under conditions at which blood-flow measurements were subsequently made are shown in Table 2.
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A, data for an individual in which increased linearly with speed until was reached. B, data for an individual in which the slope of versus speed decreased slightly before was reached. Lactate and values at 0 m s–1 were obtained while the bird rested in the dark. The lines on each plot represent a linear regression equation fitted to the initial linear portion of the relation between and increasing speed.
Cardiac output and overall distribution of flow
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Cardiac output , calculated from the dilution of the injected spheres, increased linearly with the predicted from the relations between and speed for the individual birds (Fig. 2A). Cardiac output in ml min–1 was described by the following regression: + 136, r2 = 0.93. The total flow to all the leg muscles showed a similar relation to : , r2 = 0.94 (Fig. 2A). Virtually the entire increase in cardiac output with increasing exercise intensity was accounted for by the sum of the flow to the leg muscles and heart , which is described by the regression: , r2 = 0.95 (Fig. 2A). The remaining flow, – , demonstrated no significant relation with (Fig. 2B) nor did the flow to the major organs other than the heart (sum of the flow to the brain, kidneys, testes or ovaries, spleen, pancreas and digestive tract) (Fig. 2B).
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A, total cardiac output, blood flow to the leg muscles, and blood flow to the leg muscles and heart. B, total blood flow other than to the leg muscles and heart, and total blood flow to the organs excluding the heart (brain, spleen, stomach, pancreas, intestines, caecae and kidneys). The lines are linear regressions through the data (see text for equations). The regressions in B are not statistically significant.
Heart rate and stroke volume
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Cardiac output increased by 4.9-fold going from rest to 90% of . Knowing both cardiac output and heart rate (fH), we were able to calculate stroke volume (VS). Increased cardiac output was accounted for by substantial increases in both fH and VS. We measured an increase in fH from 203 ± 11 beats min–1 at rest to 451 ± 10 beats min–1 (mean ± S.E.M., n = 5, Fig. 3A) at a speed expected to produce 90% of , a factorial increase of 2.2. Values of VS increased from 1.7 ± 0.1 ml (1.1 ml kg–1) at rest to 3.8 ± 0.1 ml (2.6 ml kg–1) at the highest running speed (mean ± S.E.M., n = 5, Fig. 3B), a factorial increase of 2.2 from rest to 90%.
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A, relation between heart rate and energy expenditure; B, relation between stroke volume and energy expenditure. Energy expenditure is expressed as a fraction of the maximal oxygen consumption, . The treadmill speeds for each condition are indicated next to the plotted values. Values are shown as means ± S.E.M., n = 5.
Blood flow to the skeletal muscles
Leg muscles. After removal of the effects of inter-animal variability (see Methods) all of the leg muscles showed significant effects of exercise level on blood flow (P < 0.001 for all muscles). However, the effects of running speed on blood flow were not uniform among the muscles. The first question we asked about these variable responses was whether flow increased significantly (Contrasts Procedure, P 0.05) as the bird went from one exercise level to the next highest level. Based on this comparison, the leg muscles were divided into three categories (Fig. 4). First, 11 muscles showed significant increases in flow across all levels of exercise from rest to a fast run (Fig. 4A). Second, nine muscles showed no significant increase in flow when walking at 0.5 m s–1 compared with resting levels, but demonstrated progressive significant increases in flow at the two higher speeds (Fig. 4B). Third, four muscles had significant increases in flow at lower speeds, but showed no significant increases in flow between running at 1.5 m s–1 and > 2.4 m s–1 (Fig. 4C).
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The muscles are divided into the following categories in terms of how flow increased in relation to increasing energy expenditure: A, muscles in which there was a significant increase in mass-specific flow with each level of increased energy expenditure from rest to a fast run; B, muscles in which there was no significant change in mass-specific blood flow between rest and walking at 0.5 m s–1, but progressively increasing blood flow at higher speeds; C, muscles which had significant increases in flow at lower speeds, but showed no significant increases in flow between running at 1.5 m s–1 and > 2.4 m s–1. Statistical significance of the difference between two successive exercise levels is indicated by: *P 0.05; **P 0.01; and ***P 0.001. Abbreviations for muscle names are given in Table 1. Means ± S.E.M., n = 5.
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To further distinguish differences in the pattern of increase in blood flow, we examined the fractional contribution of each muscle to the total increase in flow to the leg muscles with increasing exercise intensity (Fig. 5). This fractional value, termed here fractional delta flow , is defined as the increase in blood flow to an individual muscle between two successive exercise levels divided by the increase in blood flow to the total mass of hind-limb muscles between the same two levels of exercise. If flow to all the muscles contributed proportionally to the increase in overall leg blood flow across speeds, one would expect values to be constant within a given muscle and to be proportional to the mass of the muscle as a fraction of the overall mass of the leg muscles. Instead we found that the pattern of increasing flow varied greatly among the muscles. The muscles could be divided into three groups based on a statistical analysis of the transformed values of (, see Methods). Group I muscles preferentially increased their blood flow when going from a rest to a walk, and had significant (P < 0.04) decreases in values as exercise intensity increased. Group II muscles had the opposite pattern of increases in blood flow, such that the values of significantly (P < 0.02) increased with exercise intensity. Finally, Group III muscles showed no significant changes in their fractional contributions to the overall increase in leg muscle blood flow across the range of exercise intensities studied.
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is the increase in flow to an individual muscle between the two levels of exercise divided by the total increase in flow to all the hind-limb muscles. The bottom axis represents the arcsine-transformed values of ; transformed value = . This transform is non-linear as indicated by the top axis, which represents the values converted to a percentage. Muscles are divided into three groups. Group I muscles show significant decreases in the transformed values of as exercise intensity increases. Group II muscles show significant increases in the transformed values of . Group III muscles show no significant change in the transformed values of across exercise levels. Statistical significance of the difference between two successive arcsine-transformed values of is indicated by: *P 0.05; **P 0.01; and ***P 0.001. Abbreviations for muscle names are defined in Table 1.
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Skeletal muscles outside the hind limb. The intercostal muscles and the abdominal wall muscle, which are associated with respiratory movements, show a pattern of flow changes with exercise level similar to that found in the hind-limb muscles (Fig. 6). Blood flow did not change between rest and walking, but then showed progressive, significant (P < 0.05) increases at higher running speeds. The pectoralis and supracoracoideus muscles showed significant (P < 0.02) effects of exercise level in the overall model. However, in the pectoralis no paired comparisons were significant with either the Contrasts test or the Games-Howell test. The supracoracoideus showed a significant (Contrasts, P < 0.02) increase in flow, at the highest running speed, but showed no significant change in flow at lower speeds.
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Mean ± S.E.M., n = 5. The mean muscle masses (± S.E.M.) for both sides are given below the name for the flight muscles. Total masses are not given for the respiratory muscles, because the entire muscle mass was not dissected. Supracor., supracoracoideus
Blood flow to the organs
Heart. At all speeds, the ventricles had the highest mass-specific blood flow of all the tissues for which we made measurements (Fig. 7). Both the atria and the ventricles showed significant effects of exercise level in the overall model (P < 0.001). However, in the paired comparisons both the atria and the ventricles only showed significant increases (P < 0.05) between 0.5 m s–1 and 1.5 m s–1.
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Mean ± S.E.M., n = 5. The mean organ masses (± S.E.M.) are given below the name of each organ. Masses for the kidneys and reproductive (repro.) organs are the sum of right and left organs. The reproductive organs are the testes in males and the ovaries and oviduct in females.
Brain and abdominal organs. Among the organs other than the heart, only the brain and proventriculus showed significant (P < 0.03) effects of exercise level on blood flow in the general linear model (Fig. 7). The mean flow to the brain increased with increasing exercise intensity, and in paired comparisons the increase from 0.5 to 1.5 m s–1 was statistically significant (P = 0.018). Mean flow to the proventriculus increased from rest to 0.5 m s–1, and then declined at higher speeds. In paired comparisons the only significant contrast (P = 0.03) was due to the increase in flow from rest to 0.5 m s–1. Flow to the remaining organs was essentially constant at all levels of exercise.
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Blood haemoglobin and lactate. The total amount of blood withdrawn during the blood-flow experiments was less than 5% of the estimated blood volume of the birds and we saw no decrement in haemoglobin concentration with the successive blood withdrawals. The birds in the present study fell into two groups in terms of their blood haemoglobin concentrations. Two mature, egg-laying females had mean blood haemoglobin concentrations of 10.6 and 10.9 g dl–1 of blood. The remaining three birds, two males, and a younger non-ovulating female had a mean haemoglobin concentration of 15.1 ± 0.2 g dl–1 of blood (mean ± S.E.M.). In the three individuals with the higher blood haemoglobin concentration, lactate levels remained low at all exercise levels (1.10 ± 0.18 mmol l–1, mean ± S.E.M., n = 12). Low lactate levels were also found in the two egg-laying females, except at the highest running speed at which lactate levels were elevated. These two females were the birds that showed a slightly reduced slope of versus speed at speeds near (Fig. 1).
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Discussion
Blood flow as a measure of energy use by active skeletal muscle
Microsphere measures of blood flow in muscle have been used in a variety of contexts, but only recently have they been used to partition energy expenditure among individual active muscles during exercise (Marsh et al. 2004). Armstrong & Laughlin (1985) examined the use of this technique as a potential measure of muscle energy use, but were cautious because they considered that factors other than local metabolic rate might play a significant role in determining blood flow. Based on the evidence outlined briefly below, we have concluded that this technique is the most reliable method available for estimating the energy use by all of the individual active skeletal muscles during aerobically supported terrestrial locomotion.
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This conclusion is based on the observation that during aerobically supported exercise the metabolic rate of active muscle is proportional to blood flow. The available evidence from in vivo studies suggests that blood flow to active skeletal muscle is controlled so that the rate of oxygen delivery is directly proportional to the metabolic rate (Delp & Laughlin, 1998; Murrant & Sarelius, 2000; Laughlin & Korzick, 2001; Boushel, 2003). Measures of total systemic flow support the correlation of muscle blood flow and muscle . Cardiac output increases linearly with in guinea fowl (Fig. 2) and other animals (Barger et al. 1956; Stenberg et al. 1967; Grubb, 1982; Grubb et al. 1983; Armstrong et al. 1987; Snyder et al. 1999). In guinea fowl all of the increase in flow during exercise is directed toward the active skeletal muscles and the heart (Fig. 2). In humans the relation of blood flow (or oxygen delivery) to has also been found to hold for skeletal muscle flow specifically (Saltin et al. 1998), as it does for leg muscle blood flow in guinea fowl (Fig. 2).
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The magnitude of blood flow cannot always be taken as an indicator of metabolic rate. Extraction of oxygen by resting muscle is lower than active muscle, and therefore the correlation of blood flow and may not hold for resting skeletal muscles either pre- or post-exercise (Bangsbo & Hellsten, 1998; Clark et al. 2000). Blood flow should also not be used as an index of metabolic rate for organs such as the intestines, stomach and kidneys, in which regulation of flow is not tied primarily to metabolic rate.
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Of course, the correlation of blood flow with energy use only holds for aerobically supported muscle activity. In the present study, we chose a maximum exercise level equal to that expected to produce a of 90% of . In 3 of the 5 birds, this level of effort was totally aerobic, as indicated by the low lactate values. In two birds, some lactate accumulated at the highest running speed (Fig. 1). In these birds 3–5% of the energy use was supported anaerobically based on the decrease in slope of versus speed at speeds approaching (Fig. 1).
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Cardiac output and oxygen delivery to the tissues
Cardiac output. Guinea fowl had a 4.9-fold increase in cardiac output between rest and 90% . The increase in cardiac output reached during exercise in these birds is not unusual. Our resting and near-maximal values of cardiac output are close to those predicted for guinea fowl based on the equations of Grubb (1983) and Bishop (1997). Precise comparisons with empirical data on the cardiac output of other avian species is difficult because the level of energy expenditure relative to is not known for any other study of cardiac output in birds. However, studies of flying pigeons and running emus that involve large multiples of resting metabolic rate report increases in cardiac output of 4.3- and 7.2-fold, respectively (Butler et al. 1977; Grubb et al. 1983).
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Although the increase of cardiac output achieved by exercising guinea fowl is not unusual, the relative contribution of stroke volume in achieving this increase (Fig. 3) is greater than has been found in most other studies of birds and mammals. Several other species of birds show little or no increase in stroke volume during treadmill exercise (Grubb, 1982; Grubb et al. 1983; Kiley et al. 1985; Fedde et al. 1989) or during wind-tunnel flights (Butler et al. 1977). The emu has a substantial increase in cardiac output during submaximal exercise (Grubb et al. 1983), but the increase in heart rate still dominated in increasing cardiac output. This pattern of predominantly heart rate-mediated changes in cardiac output is also typical of mammals (Armstrong et al. 1987; Taylor et al. 1987; Jones et al. 1989), except for elite human athletes, who exhibit a doubling of stroke volume between rest and maximal exertion in conjunction with a factorial increase in heart rate of 2.6 (Zhou et al. 2001).
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Blood flow to the viscera. The blood flow to the tissues of the guinea fowl other than the heart and working skeletal muscles remains constant as exercise intensity increases (Fig. 2). A related bird, the domestic fowl, also shows no evidence for redistribution of blood flow away from the gut during exercise (Brackenbury et al. 1990, 1993). In many other vertebrates studied to date, visceral blood flow decreases during exercise (Rowell, 1974; Laughlin & Armstrong, 1982; Manohar, 1986; Armstrong et al. 1987). In dogs the response of visceral flow to exercise is mixed (Musch et al. 1987). The lack of diversion of visceral flow in our study could reflect the fact that our birds were endurance trained. Endurance training in rats blunts, but does not eliminate, the redistribution of visceral flow (Armstrong & Laughlin, 1984). However, the lack of diversion of visceral flow in guinea fowl may also be an evolutionary consequence of their ecology. When locomotion is a regular part of daily activity, as it is in guinea fowl (Ayeni, 1982), then redistribution of flow could incur a significant cost in terms of slower digestion and impaired excretion.
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Oxygen delivery to the working skeletal muscles and heart. Birds require greater blood flow to the working muscles for a given increase in metabolic rate than do mammals. Grubb (1982) found that the slope of cardiac output versus was higher in pigeons and ducks than had been found previously for mammals and suggested that birds might have lower extraction efficiencies than mammals. The higher slope is confirmed by other studies in birds (Butler et al. 1977, 1988; Bech & Nomoto, 1982; Grubb, 1983). Birds must pump approximately 10 ml of blood for every 1 ml increase in oxygen consumed, whereas typical values for mammals are 5–7 ml of blood for each millilitre increase in oxygen use (Barger et al. 1956; Stenberg et al. 1967; Armstrong et al. 1987; Saltin et al. 1998; Snyder et al. 1999). The slope of the relation of blood flow to should be inversely proportional to the extraction efficiency of the active muscles (Wolff, 2003). We did not measure the oxygen content of the blood directly in the present study. However, using the slope of leg blood flow versus for guinea fowl and the measured haemoglobin concentration, we estimate that the extraction efficiency of the guinea fowl leg muscles was approximately 60%. Similar calculations from studies of birds in which blood oxygen content was measured directly yield estimated extraction efficiencies for avian skeletal muscle of 67–73% (Butler et al. 1977; Bech & Nomoto, 1982; Grubb, 1983). Extraction efficiencies for mammalian muscles estimated similarly typically range from 80 to more than 90% (Taylor et al. 1987). The reasons for the lower extraction efficiencies in avian muscle as compared with mammalian muscle are not known, but one factor could be the characteristics of the large nucleated red blood cells found in birds.
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Probably because of this lower extraction efficiency, the mass-specific flow to the ventricles and skeletal muscles of guinea fowl were very high when the birds were exercised at levels approaching . The values reported here for the ventricles and some skeletal muscles, e.g. the PIFL, are the highest values of mass-specific blood flow reported for muscle. The limited other data available for other avian muscles suggest that these values are not unrealistically high. In swimming ducks, Butler et al. (1988) report a flow to the heart of 3.6 ml g–1 min–1, a mean flow to the leg muscles of 3.4 ml g–1 min–1, and a maximal flow to one highly aerobic muscle of 8.6 ml min–1 under conditions eliciting increases in cardiac output of 1.7-fold. Similarly, Duchamp & Barre (1993) reported blood flow to the heart of 5–7 ml g–1 min–1 during cold exposure that elicited an increase in cardiac output of only 1.3- to 1.6-fold. Our maximal values were higher than these literature values for the ventricles (14.5 ml g–1 min–1, and some skeletal muscles, e.g. 9.6 ml g–1 min–1 for the PIFL (Fig. 4), but our peak exercise levels required an increase in cardiac output of nearly 5-fold.
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Partitioning energy use with increasing exercise intensity
Cardiac muscle and respiratory muscles. Energy use increases linearly with speed in guinea fowl as it does in other animals using terrestrial legged locomotion (Ellerby et al. 2003). Examining the distribution of blood flow allows us to partition this energy expenditure among various muscles. Approximately 90% of the increase in cardiac output with exercise in guinea fowl serves to supply blood to the working hind-limb muscles (Fig. 2), with the remaining approximately 10% providing increased flow to the coronary circulation and the respiratory muscles. The heart receives between 8 and 9% of the increase in flow, which is higher than values estimated from studies on mammals (Armstrong et al. 1987; Musch et al. 1987). The amount of blood flow distributed to the respiratory muscles of guinea fowl is quite small. We do not have precise measures of the total respiratory muscle mass in guinea fowl, but based on the increase in flow from rest to exercise that is not accounted for by flow to the heart and hind-limb muscles, the respiratory muscles receive only approximately 2% of the increase in blood flow from rest to 90% of .
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Fractional contributions of individual hind-limb muscles to total energy use. Increasing energy use (as indicated by blood flow) with increasing running speed differs greatly among the hind-limb muscles. We divided the muscles into three groups based on their fractional contributions to the total increase in flow ( values) with increasing exercise intensity. Group I muscles are muscles that preferentially used energy at the lower locomotor speeds and decreased their fractional contributions to the increase in energy use at greater speeds (Figs 5 and 8). The five muscles in Group I made up just 25% of the hind-limb muscle mass, but these muscles were responsible for 57% of the increase in energy use between rest and walking at 0.5 m s–1 (35% of ). However, the contribution of these muscles to the increase in energy use as the birds go from 63 to 90% of is only 14%. Decreasing values do not mean blood flow decreased in these muscles at high speeds (Fig. 8B), but that the further increase in total flow to the leg muscles at high speeds was due to increases in flow to other muscles. Of the muscles in Group I only the ITC showed a statistically significant increase in flow as organismal energy use was increased from 63 to 90% of . The four large muscles and five small muscles in Group II showed the opposite pattern of increases in energy use to those in Group I (Figs 5 and 8). Group II muscles make up 46% of the hind-limb muscle mass, but are responsible for only 15% of the increase in energy use as the birds go from rest to walking at 0.5 m s–1. Conversely, at high levels of exercise, the running bird is increasingly dependent on these muscles. They are responsible for 58% of the increase in energy use as metabolic rate increases from 63 to 90% of . Group III muscles increase their energy use approximately in proportion to the overall increase in metabolic rate across all exercise levels (Figs 5 and 8). They make up approximately 28% of the hind-limb muscle mass and contribute approximately the same percentage to the increase in energy use across all exercise levels.
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The muscles are divided into groups based on the statistical differences in transformed values of (Fig. 5). Group I muscles are muscles that disproportionately receive blood flow at lower running speeds and receive less of the increase in flow at higher speeds. Group II muscles are muscles that increase their contributions to the increment in flow at the higher speeds. Group III muscles maintain approximately the same contribution to the increase in flow over all increments in exercise intensity. In A the height of the bar for each muscle (or muscle group) represents the percentage of the total increase in flow between exercise levels that is accounted for by flow to that muscle. In B the height of the bars for each muscle represents the absolute flow in ml min–1. To simplify the figure, flows to the three heads of the gastrocnemius (GAST) were combined, as were the flows to some small muscles with small individual contributions to the overall flow; Sm. Mus. II are ITCR, CFP, ILPR, CFC, Amb.; Sm. Mus. III are ISF, OM, DE. Abbreviations for individual muscles are given in Table 1.
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Our data show large differences in energy use by agonistic muscles within and across running speeds in guinea fowl. This suggests that the common practice of distributing work or force estimated from external measures across a group of agonists based solely on the mass or cross-sectional area of the muscles should be viewed with caution. For example, among the ankle extensors in the guinea fowl, the Group I digital flexor and FL muscles are approximately the same size as the Group III gastrocnemius muscles. However, largely due to the substantial contributions of the digital flexors, the Group I muscles used more than 70% of the energy consumed by ankle extensors when guinea fowl walked at 0.5 m s–1. Among the hip extensors the Group I muscles (PIFM, PIFL and ITC) together have a mass of only 36% of the Group II and III hip extensors (ILPO, FCLP, FCLA, posterior IF, CFP and CFC). However, the Group I hip extensors consumed 2.7 times the energy during walking as did the Group II and III hip extensors. Because of the opposite patterns of increasing energy use with speed in Group I and II muscles, the distribution of total energy use among the agonistic muscle groups shifts with increasing speed. At the highest speed tested, the distribution of energy use approximates the distribution of mass among these muscles.
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The differences in the pattern of increasing blood flow with increasing exercise intensity among the hind-limb muscles are correlated with probable differences in fibre type. Other studies of muscle blood flow have found that at low exercise intensities increases in blood flow are seen preferentially in regions of muscles with slow-oxidative fibres. Areas rich in fast-oxidative fibres show increases in flow at intermediate exercise intensities, and areas where fast-glycolytic fibres predominate show the greatest increases in flow at the most intense levels of aerobic exercise (Laughlin & Armstrong, 1985). In guinea fowl, blood flow to the puboischiofemoralis medialis muscle (PIFM) increases 16-fold between rest and the slowest speed tested and shows only a modest increase in flow with increasing running speed (Fig. 4). In the domestic fowl and quail, which are members of the same family of birds, this muscle has the highest proportion of slow fibres of any thigh muscle (Suzuki & Tamate, 1979; Suzuki et al. 1985; Crow & Stockdale, 1986). The PIFL, another group I muscle, is made up almost exclusively of fast-oxidative fibres, and is lacking in fast-glycolytic fibres in the domestic fowl (Suzuki et al. 1985). The flexor cruris lateralis pars pelvica (FCLP) and the posterior part of the iliotibialis (ILPO), which are Group II muscles, show a very different pattern of increasing blood flow. Flow to these muscles does not increase significantly relative to rest when the birds were walking at 0.5 m s–1 but increases greatly at higher speeds (Figs 5 and 8). The association of these muscles with higher-speed locomotion suggests that they should mainly be composed of fast oxidative and glycolytic muscle fibres, with few slow-oxidative fibres. In the domestic fowl the FCLP is entirely lacking in slow-oxidative fibres, containing approximately 30% fast-oxidative-glycolytic fibres and 70% fast-glycolytic fibres, and the ILPO has a similar composition (Suzuki et al. 1985). No data are available for the fibre composition of the FCLA. Group III muscles, which show progressive increases in values with speed, are predicted to contain a mix of fibre types ranging from slow oxidative to fast glycolytic that would be recruited across the range of exercise intensities from walking to running at near . This fibre composition appears to be the case for the muscles in this group for which fibre type data are available on related species of running birds. The IC, FCM and LG all have a mix of slow-oxidative, fast-oxidative-glycolytic and fast-glycolytic fibre types (Kiessling, 1977; Suzuki et al. 1985).
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Swing versus stance. Our primary interest in studying blood flow during running is to be able to relate the energetics of muscle use to the mechanical function of the individual muscles used. Most studies attempting to integrate muscle function and energy use during running have inferred the relations between these variables from externally measurable biomechanics and organismal energy use. Knowing the distribution of energy use among the individual muscles should allow us to better understand information on the in vivo function of the muscles. For example, Marsh et al. (2004) used blood flow data on guinea fowl to conclude that swinging the limbs consumes approximately a quarter of the energy expended during locomotion, independent of running speed. Across the three walking and running speeds tested, the individual muscles differ in the fraction of total hind-limb flow that they receive, but the sums of the flows to the swing and stance phase muscles represent remarkably constant fractions of the total flow (Marsh et al. 2004).
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Our estimate from the proportion of blood flow to the swing-phase muscles can be combined with data on the metabolic cost of running (Ellerby et al. 2003) and estimates of the segmental mechanical work required to swing the limbs in guinea fowl (Fedak et al. 1982) to calculate the mechanical efficiency of the swing-phase muscles. These estimates of efficiency range from approximately 6% when the birds walk at 0.5 m s–1 to approximately 13% at the highest running speed. These values for efficiency, particularly the value for walking, are considerably lower than the maximal values cited for aerobically functioning muscle of approximately 25%. One likely explanation for the low efficiency is that the swing phase muscles are unlikely to purely be doing positive work. At the start of the swing phase the direction of motion of the limb is reversed. Work must be absorbed to decelerate the limb; subsequently work is done on the limb to lift and accelerate the limb forwards. Muscles are also probably actively stretched to absorb work and act as brakes to decelerate the limb before the foot touches the ground. However, the possibility also exists that more mechanical work is done than that calculated by Fedak et al. (1982). Better estimates of work at the joints in the limb might be obtained from inverse dynamics calculations on birds under similar experimental conditions to those employed in our blood-flow studies.
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Supporting body weight. A complete analysis of the relations between the energy use of muscles and their mechanical functions is not possible with the current state of knowledge, but some observations can be made that indicate the usefulness of having an estimate of individual energy use. First, our data indicate that the cost of supporting body weight at the knee in guinea fowl may be less than the cost at the hip or the ankle, particularly during walking. Two large muscles active during stance serve as knee extensors, the FT and the ILPO. The ILPO is also a hip extensor and thus can also contribute to supporting body weight at the hip. The average moment arm of the ILPO at the hip is larger than at the knee. The FT and ILPO consume just 11% of the energy used by the hind limb during walking. At higher speeds the energy use by the FT and ILPO increases because of increases in the fraction of flow distributed to the ILPO. These two muscles consume 22 and 24% of the hind-limb energy use when the exercise level is 63 and 90% of , respectively. The actual cost for weight support during stance is likely to be less than these values indicate for several reasons. First, the FT is also active during swing phase (Marsh et al. 2004). Second, the FT and the ILPO probably also perform some positive work during stance phase. Finally, a portion of the force production of these muscles must also be used to counteract the knee flexors that are co-activated (see below). Muscles with hip extensor moments consume between 39 and 42% total energy used by the locomotor muscles, and the ankle extensors consume between 27 and 32%. Of course, these muscles probably also perform functions other than just weight support. For example, the FL in turkeys has been shown to produce significant amounts of work during level running (Gabaldon et al. 2004).
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Performing work. Our data indicate that substantial energy is used by muscles performing positive work in running guinea fowl. Previous investigators have suggested that supporting body weight is the main function of the leg muscles and that this could be done most economically by muscles operating nearly isometrically as ‘struts’ tensioning the elastic tendons that absorb and release the mechanical work required in every stride (Kram & Taylor, 1990; Taylor, 1994; Roberts et al. 1997). Some muscles have been found to operate in this manner, including the LG in running birds (Roberts et al. 1997; Daley & Biewener, 2003) and the ankle extensors of hopping wallabies (Biewener et al. 1998). These lower limb muscles are suited to this function in that they have relatively short fascicles and long tendons. However, many of the thigh muscles of running mammals and birds have relatively long fascicles and short tendons. This architectural design requires the active fascicles in these muscles to track the movements of the joints. In the guinea fowl many of these muscles are predicted to shorten while active based on joint angles and EMG activity (Gatesy, 1989), and data from our laboratory confirm this pattern of activity using sonomicrometry and EMG recordings (Buchanan, 1999; R.L. Marsh, D.J. Ellerby, J.A. Carr & T.A. Hoogendyk, unpublished observations). Additionally, some of the muscles with architectures similar to the LG may also do considerable work in level running, e.g. the FL (Gabaldon et al. 2004). Our preliminary estimate is that in level running guinea fowl at least 45% of the energy is used by leg muscles performing positive work, and this estimate may be considerably higher if short-fascicled muscles other than the FL also do positive work.
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Stability and co-contraction. Recent studies of legged terrestrial locomotion have emphasized the importance of stability and its possible costs (Seipel et al. 2004; Donelan et al. 2004). Because the mechanical functions of muscle that lead to stability have not been unequivocally identified, we cannot partition the cost of stability using our blood-flow data and available data on in vivo function. However, we can put a preliminary estimate on the cost of co-contraction, which has been suggested to be one important aspect of stabilizing certain joints (Wagner & Blickhan, 2003). In the guinea fowl, co-contraction is a conspicuous feature of muscles operating around the knee. The knee extensors, the FT and the ILPO, are simultaneously active with knee flexors, the FCLP, FCM, IF, IG and LG. We estimate that these co-contracting muscles consume between 15 and 30% of the total energy used by hind-limb muscles over the range of speeds studied. (This sum was done after reducing the contributions of the FT and IF by 50% to account for their swing phase activity and reducing the contribution of the FCLP by 50% to account for its activity in late swing as a pure hip extensor in combination with the FCLA.) The cost of co-contraction may be higher because portions of other lower leg muscles also act as knee extensors or flexors including the MG, the FL and several of the digital flexors. Of course it could also be argued that because many of these muscles cross multiple joints they are also consuming energy to carry out other functions. Nevertheless, our data illustrate the potential cost of stabilizing the knee during bipedal running in birds.
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Conclusions
First, overall blood flow to the leg muscles in running guinea fowl increased in proportion to metabolic rate. This result and considerable data from the literature are consistent with using blood flow as an effective indicator of energy expenditure by active skeletal muscles during aerobically supported locomotion. Measuring blood flow seems to be the only practical way of estimating the energy consumption of individual skeletal muscles in freely locomoting animals in which many muscles are active.
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Second, guinea fowl were unusual in their overall cardiovascular response to exercise in two respects. The increase in cardiac output associated with exercise was achieved by equal factorial changes in stroke volume and heart rate, rather than the mainly heart rate-mediated changes shown in most other birds and mammals. Also, blood flow to the viscera was maintained during exercise, in contrast to the redistribution of flow to the active muscles measured in all other species for which data are available.
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Third, when the birds exercised at 90% of , the values of mass-specific blood flow to the heart and some of the hind-limb muscles were the largest reported for any muscles. However, they are consistent with the limited data available for other birds exercising at lower aerobic intensities. The large flows are probably due to the demanding level of exercise performed and the fact that birds apparently have lower extraction efficiencies in their muscles; thus they require greater amounts of muscle blood flow per amount of oxygen consumed.
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Fourth, approximately 90% of the increased cardiac output during exercise was directed to the hind-limb muscles. The patterns of increasing flow, and by inference energy use, with increasing speed varied greatly between different muscles. The muscles were divided into three groups depending on whether their blood flow increased preferentially at low speeds, high speeds or equally across all speeds. As expected from previous work in mammals, these patterns were correlated with the likely fibre type distribution of the muscles (based on information on related species of birds).
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Fifth, the differences in energy use we have demonstrated within groups of agonistic muscles have important implications for biomechanical models that must specify the distribution of activation among agonists. Knowing the distribution of energy use among muscles with different actions will allow the evaluation of future inverse dynamics calculations that predict the force, work, and power required at each joint.
Six, our data, when combined with available data on the mechanical functions of individual muscles, enabled the energetic costs of various mechanical functions to be estimated from the partitioning of blood flow among the muscles. The estimated energetic costs of swinging the limbs were approximately a quarter the total energetic costs of locomotion, regardless of speed. Co-contraction of muscle agonists at the knee accounted for approximately 15–30% of the energy costs of locomotion. Substantial energy costs were also suggested to be associated with the production of positive work. This information contrasts with one of the prevailing views of terrestrial locomotor energetics in which the energetic costs of force production during stance are thought to account for most of the energy use.
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Abstract
Running and walking are mechanically complex activities. Leg muscles must exert forces to support weight and provide stability, do work to accelerate the limbs and body centre of mass, and absorb work to act as brakes. Current understanding of energy use during legged locomotion has been limited by the lack of measurements of energy use by individual muscles. Our study is based on the correlation between blood flow and aerobic energy expenditure in active skeletal muscle during locomotion. This correlation is strongly supported by the available evidence concerning control of blood flow to active muscle, and the relationship between blood flow and the rate of muscle oxygen consumption. We used injectable microspheres to measure the blood flow to the hind-limb muscles, and other body tissues, in guinea fowl (Numida meleagris) at rest, and across a range of walking and running speeds. Combined with data concerning the various mechanical functions of the leg muscles, this approach has enabled the first direct estimates of the energetic costs of some of these functions. Cardiac output increased from 350 ml min–1 at rest, to 1700 ml min–1 at a running speed (2.6 m s–1) eliciting a of 90% of . The increase in cardiac output was achieved via approximately equal factorial increases in heart rate and stroke volume. Approximately 90% of the increased cardiac output was directed to the active muscles of the hind limbs, without redistribution of blood flow from the viscera. Values of mass-specific blood flow to the ventricles, 15 ml min–1 g–1, and one of the hind-limb muscles, 9 ml min–1 g–1, were the highest yet recorded for blood flow to active muscle. The patterns of increasing blood flow with increasing speed varied greatly among different muscles. The increases in flow correlated with the likely fibre type distribution of the muscles. Muscles expected to have many high-oxidative fibres preferentially increased flow at low exercise intensities. We estimated substantial energetic costs associated with swinging the limbs, co-contraction to stabilize the knee and work production by the hind-limb muscles. Our data provide a basis for evaluating hypotheses relating the mechanics and energetics of legged locomotion.
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Introduction
Linking mechanics and energetics has been a major goal of integrative studies of locomotion, but this goal has remained elusive for terrestrial legged locomotion. The diversity of function of the limb muscles used in legged locomotion has been highlighted in studies that measure mechanical function of active muscles using sonomicrometry and electromyography. Some muscles have been found to operate with little length change during force production, for example, the lateral gastrocnemius in running turkeys (Roberts et al. 1997) and the semimembranosus in running dogs (Gregersen et al. 1998). Other muscles have been shown to actively shorten and perform work, e.g. the long-head of the triceps brachii in running dogs (Gregersen et al. 1998), and the fibularis longus in running turkeys (Gabaldon et al. 2004). Still other muscles are lengthened while active, acting as brakes to absorb work, e.g. the iliofibularis muscle in running guinea fowl (Buchanan, 1999; Marsh, 1999). Muscles have also been found to perform different mechanical functions in different gaits, or between contralateral limbs in an asymmetrical gait (Gillis & Biewener, 2001). Unfortunately, the diversity of mechanical function being revealed in these studies does not lend itself to any simple conclusions about the links between mechanical function and energy use. To establish such links, the mechanical function of individual muscles needs to be connected to their energy use. In other words, we need to measure the increase in energy use by individual muscles during running.
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We have recently suggested that blood flow to individual active muscles as measured with injected microspheres may represent the most reliable alternative for indicating their energy use during exercise (Marsh et al. 2004). Strong evidence is available indicating that the rate of blood flow to an active muscle provides a reliable indication of its aerobic energy expenditure. The increase in local vascular conductance associated with skeletal muscle activity is under the control of a number of complexly interacting factors, primarily local in origin (reviewed by Koller & Kaley, 1991; Segal, 1994; Radegran & Hellsten, 2000; Laughlin & Korzick, 2001; Ellsworth, 2004). These local mechanisms lead to an increase in flow in response to changing exercise intensity that is compatible with the known recruitment order of fibre types (Laughlin & Armstrong, 1982; Armstrong & Laughlin, 1983, 1985; Manohar, 1986; Armstrong et al. 1987), and is proportional to the increase in metabolic rate (Delp & Laughlin, 1998; Murrant & Sarelius, 2000; Laughlin & Korzick, 2001; Boushel, 2003).
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We have used blood flow measured with coloured microspheres as our proxy measure for energy use by the skeletal muscles used in walking and running. Microsphere measures of blood flow have been validated extensively and used to measure muscle blood flow in a number of studies that focused mainly on mass-specific blood flow and its control in relation to such parameters as fibre type and training regime (Armstrong, 1988). Microsphere blood-flow measurements have only recently been used to estimate the total energy use by all the individual muscles used in locomotion (Marsh et al. 2004).
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The aim of the present study was to measure how blood flow, and by inference, energy expenditure, was altered in all the muscles of the avian hind limb with increasing exercise intensity during walking and running. Improving our understanding of muscle energy use during legged locomotion will have important implications in several areas. First, we can begin to quantify the energetic cost of the diverse mechanical functions served by the various muscles activated during walking and running (Marsh et al. 2004). Second, better knowledge of distribution of energy use will allow the evaluation of inverse-dynamics calculations that attempt to relate externally measured forces and movements to underlying muscle function. Third, understanding the distribution of energy use allows the evaluation of hypotheses that attempt to explain the patterns of energy use seen with varying speed and body size (Marsh et al. 2004).
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We measured blood flow during treadmill exercise in helmeted guinea fowl (Numida meleagris). In the wild, members of this species mostly walk and run as part of their daily activities and fly only to escape predation (Ayeni, 1982). Domesticated strains of guinea fowl have retained a considerable running capacity and are capable treadmill runners, having been used in several laboratory investigations of running (Fedak et al. 1974; Roberts et al. 1998; Gatesy, 1999a,b; Buchanan & Marsh, 2001; Daley & Biewener, 2003). Some of the data presented here have previously been used to determine the relative division of energy expenditure between stance and swing phases of the stride (Marsh et al. 2004). In the present study, a more complete description of how absolute blood flow to individual muscles changes with running speed is given. Cardiac performance data, and blood flows to tissues and organs other than the leg muscles are also presented. Additionally, the data provide a baseline for comparing data on muscle blood-flow experiments in which locomotor function is altered, for example by load carrying or uphill running.
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Methods
Animals and training protocol
Guinea fowl (Numida meleagris) were obtained from The Guinea Farm (New Vienna, IA, USA) as hatchlings and cage reared at the Northeastern University Division of Laboratory Medicine. At the time of the measurements, the birds were between 10 and 14 months old. Birds had ad libitum access to food and water and were maintained on a 12 : 12 h light dark cycle. Mean body mass was 1.48 ± 0.06 kg (± S.E.M., n = 5, range 1.32–1.68 kg, 3 female, 2 male). The Northeastern University Institutional Animal Care and Use Committee approved all procedures involving live animals.
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For 2 months prior to measurements of and blood flow, we trained the birds to run on a motorized treadmill (Trimline 2600, Trimline, USA, 120 cm x 44 cm tread area). During training and subsequent experiments, the guinea fowl ran inside a three-sided box with a partial top. The box was open at the back and had a mirror mounted on the front facing the running bird. A duct brought cool air from an air conditioner to one side of the box. Airflow was controlled with a slide such that sufficient cool air was allowed to flow into the box to prevent the birds from continuously panting. The training regime consisted of running for approximately 30 min per day, 5 days a week at speeds ranging from 1.5 to 3.3 m s–1. At the end of the training period all birds could sustain 30 min of exercise at 2.5 m s–1.
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Oxygen consumption during running
We measured the rate of oxygen consumption at rest and at a range of running speeds in each bird. This procedure allowed us to measure the individual maximum aerobic capacity of the birds. Subsequently, this allowed us to run the birds at known proportions of during blood-flow measurements. Respiratory gases were collected using a flow-through respirometry system with the birds wearing a loose-fitting mask. Details of the respirometry equipment and procedures are the same as those used by Ellerby et al. (2003) and Marsh et al. (2004).
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Surgical procedures prior to the blood-flow measurements
We anaesthetized the bird using isoflurane and maintained body temperature using a heat mat placed under the bird. Using standard surgical techniques, the brachial arteries were sequentially isolated and cannulated using custom-made polyurethane, saline-filled cannulae. The cannula in the right brachial artery was advanced until its tip was in the left ventricle, as indicated by recording blood pressure. The ventricular cannula was subsequently used to inject microspheres. The cannula in the left brachial artery was advanced to a depth such that the tip of the cannula was located in the brachiocephalic artery. The cannulae were secured in the vessels and the incisions closed with standard surgical techniques. The plugged cannulae were sutured to the skin near the elbow and left coiled under a covering of Vetrap (3M). Blood-flow measurements were carried out on the day after the surgery.
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Microsphere injections
Blood-flow measurements were made using 15 μm diameter injectable polystyrene microspheres (Triton Dye-Trak VII+, Triton Technologies, CA, USA). The microspheres were supplied suspended in sterile physiological saline containing 0.05% Tween-80 and 0.01% thimerosal at a density of 3 million per ml. For each batch of spheres of each colour, we determined a standard curve of absorption versus wavelength (325–650 nm) based on the dye extracted from a known mass of the well-mixed suspension of spheres. Microsphere suspensions were vortexed for 45 s prior to being drawn up into the injection syringe to ensure even suspension of the spheres. Syringes were weighed to the nearest milligram before and after filling to give a precise measure of the amount of microsphere solution in the syringe. The amount of spheres actually injected was determined from the amount in the syringe minus the residual spheres remaining in the syringe tip and the injection stopcock. Approximately 1 x 106 spheres were injected at each level of exercise.
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Microsphere injections were made at four levels of metabolic effort: (1) at rest in a darkened box; (2) treadmill exercise at 0.5 m s–1 (a walking gait); (3) treadmill exercise at 1.5 m s–1 (a readily sustainable running speed); and (4) treadmill exercise at a speed at which was approximately 90% of (2.5–2.78 m s–1). Prior to determining resting blood flow the bird was left in a darkened box for 10 min, sufficient time for heart rate and (based on separate measurements) to stabilize. In the box the birds sat quietly with their legs folded under themselves. After being removed from the box the birds walked for at least 2 min at 0.5 m s–1 before the experimental sequence of runs was initiated. Following this initial run the sequence of speeds for microsphere injections was: (1) high-speed running, (2) 1.5 m s–1, and (3) 0.5 m s–1. Between each test speed the bird walked for 2 min at 0.5 m s–1. The bird maintained each test speed for 2–3 min prior to the injection of microspheres. This time period was well beyond the time required for and heart rate to stabilize.
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The reference blood withdrawal was made into a heparinized syringe at a rate of between 1.0 and 1.75 ml min–1 using a syringe pump (Genie YA-12, Kent Scientific, CT, USA). Higher withdrawal rates were used at the higher levels of exercise, during which elevated cardiac output results in greater dilution of the spheres in the blood withdrawal. Approximately 10 s after starting the blood withdrawal, the microspheres were injected via the ventricular cannula. The injections were made through a Luer port of a sterile 3-way stopcock. A Millar SPR-407 pressure transducer was connected to a second Luer port to measure ventricular pressure at all times except during the injections. The microspheres were introduced as a bolus over a 20-s period. The cannula was immediately flushed with 0.7 ml of sterile saline to ensure that all the microspheres had been injected into the ventricular blood. The reference withdrawal was continued for sufficient time after completing the flush to clear all blood containing microspheres from the withdrawal cannula (30 s). The total amount of blood withdrawn was typically 1.2–1.6 ml. Residual spheres were visible in the injection syringes and injection stopcocks. New stopcocks were used for each injection to prevent cross contamination of microsphere colours. We rinsed the stopcocks and injection syringes with 100% ethanol into 50 ml centrifuge tubes to recover residual spheres for analysis.
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Blood haemoglobin and lactate concentrations
After completion of the reference withdrawal, a further 120 μl of blood was taken from the withdrawal cannula to determine blood haemoglobin and lactate concentrations. Haemoglobin concentration was measured using a Drabkin's reagent kit (Sigma Chemical Company, 525A). Lactate concentration was measured using an enzymatic UV kit (Sigma Chemical Company, 826B).
Microsphere recovery
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After completion of microsphere injections the animals were killed by an overdose of pentobarbital solution. All the muscles from one hind limb were dissected and weighed. Selected muscles from the contralateral limb were also taken as a check that the microspheres were adequately mixed in the ventricle and distributed symmetrically in the systemic circulation. We also removed and weighed all the major abdominal and thoracic organs, the brain, the flight muscles, and portions of the inspiratory and expiratory muscles. The entire mass of each hind-limb muscle was processed. Muscles larger than 10 g were subdivided between several tubes. The large pectoralis and supracoracoideus muscles were subsampled from three locations and the total mass recorded to enable calculation of total flow to the muscle. The whole mass of the brain, thoracic and abdominal organs were processed except for the liver and gizzard, which were sub-sampled, and the total flow calculated from the mass-specific flow and the total mass of the organ.
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Prior to any further processing a known amount of microspheres of a colour (navy) not injected into the animal was placed in each tube with the tissue or blood samples. This additional colour acted as a control to correct for the loss of microspheres during processing (Chien et al. 1995). Final sphere amounts were referenced to the mean number of navy spheres recovered from four unprocessed tubes containing navy spheres and scaled accordingly. Typically 70–90% of the microspheres were successfully recovered from the processed tissue samples.
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The microspheres were recovered using a modified version of the protocol recommended by Triton Technologies (see online supplement to Marsh et al. 2004). Following extraction, we weighed the tubes containing the dry microsphere pellets, added cellosolve acetate, and re-weighed the tubes to precisely determine the solvent volume. The spheres were resuspended with a vortex mixer to ensure complete dye extraction. After centrifugation to pellet out any debris the absorbance spectra of the dye mixtures were measured using a scanning spectrophotometer (Ultrospec 3300pro, Amersham, USA). Samples were scanned across a range of wavelengths from 350 to 725 nm. The numbers of microspheres of the four experimental colours and the navy process control were calculated from absorbances measured at six wavelengths representing the peak absorbances of each colour in the sample and the peak absorbance of a rare low-wavelength contaminant. Calculations were done using a matrix inversion technique implemented in Microsoft Excel. The Excel implementation was a modified version of one supplied by Triton Technology.
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Calculation of blood flow
The number of microspheres in the reference blood withdrawal was used to calculate the mass-specific tissue flow rate in ml g–1 min–1 according to:
where is the reference blood withdrawal rate in ml min–1, Nt is the number of spheres in the tissue sample, Nb is the number of spheres in the reference blood sample, and Mt is the mass of the tissue. The number of spheres collected in the withdrawal sample can also be used to calculate cardiac output as follows:
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where Ni is the number of spheres injected.
Validation of microsphere measures of blood flow in guinea fowl
We injected the microspheres into the left ventricle, a technique that has been shown to provide adequate mixing in mammals (Buckberg et al. 1971). To assess mixing, we measured the flow to several of the leg muscles bilaterally in most birds. On average, the flow to the muscles on both sides was equal. The slope of the regression of right-side muscle blood flow versus left-side total muscle blood flow was 0.98 ± 0.04 (slope ± 95% confidence interval (C.I.), n = 40). Using flow to the kidneys to assess bilateral flow, as has been done in some mammalian studies, is potentially problematical in birds because of the complex nature of the renal circulation with possibly variable amounts of renal-portal flow due to valving (Odlind, 1978), and the possible capture of microspheres entering the renal portal circulation after passing through arterio-venous shunts in the legs (Wolfenson, 1983). Even after correcting for differences in kidney mass, our measurements of arterial flow to the kidneys showed somewhat greater variability in bilateral distribution as compared with the values for the leg muscles, and a slight bias toward greater flow to the left kidney. The slope of the regression of right-side kidney blood flow versus left-side kidney blood flow (mass-specific) was 0.93 ± 0.06 (slope ± 95% C.I., n = 20).
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Our data indicate that in guinea fowl most of the microspheres were trapped in the first pass through the systemic circulation, as was found by Boelkins et al. (1973) for 15 μm spheres in the domestic fowl (Gallus domesticus). In the present study a mean of 4.2 ± 1.0% of the injected spheres were recovered from the lungs. The highest percentage of injected spheres appearing in the lungs occurred at rest with lower amounts at the higher speeds. The data of Wolfenson (1983) on the domestic fowl suggest that most of the spheres lodging in the lungs derive from systemic flow that passes through arterio-venous anastomoses in the skin in which 15 μm spheres are not trapped. The avian lung is known to trap 15 μm spheres completely (Odlind, 1978). After correcting for the spheres recovered in the lungs, we were able to account for all of the cardiac output except a mean of 140 ± 23 ml min–1 across all exercise levels. This flow presumably represents the flow to the parts of the body not analysed in this study. The percentage of the cardiac output accounted for increased with speed and reached a mean value of 95% at the highest speed.
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Under our experimental conditions, the microsphere injections were unlikely to have impaired normal function of the cardiovascular system. Heart rate and blood pressure were not influenced by the injections and the number of muscle capillaries blocked was small. For example, the mean total number of spheres of all colours accumulated per gram of gastrocnemius muscle was 4300. Assuming 1-mm long capillaries and a capillary density similar to that found in related galliform birds (Snyder, 1990), we estimate that approximately 0.5% of the total capillaries were blocked in our experiments.
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We verified the accuracy of our matrix inversion calculations and the adequacy of our extraction procedures by measuring known mixtures of spheres of different colours either alone or with control muscle samples. Previous work has indicated that at least 400 spheres must be recovered in a tissue sample to restrict errors in measured flow to less than 10% (Buckberg et al. 1971). In the present study we recovered at least 400 spheres of all colours in all tissue samples except for the spheres injected at rest for three of the smallest muscles, the CFC, CFP and the AMB.
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Statistical analyses
Analysis of variance was conducted using the general linear model in the application SPSS (versions 10 and 11 for the Macintosh) to test for significant differences in blood flow between different levels of exercise. Significant variation existed in the level of flow among animals. Consequently, an animal identifier was entered as a factor in the model along with the exercise level. This model allowed us to evaluate consistent patterns of changing flow with exercise level after removing the variation among the individual animals. To assess whether significant increases in flow occurred with increasing exercise intensity, we examined planned contrasts between adjacent levels of exercise (rest to 0.5 m s–1; 0.5–1.5 m s–1 and 1.5 to > 2.4 m s–1). As the contrasts procedure assumes equal variances, which was not the case for all tissues tested, we examined cases of borderline significance using the Games-Howell post hoc comparison, which does not assume equal variances.
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To examine the changes in fractional contribution of the individual muscles to the increases in flow between exercise levels, we transformed the fractional values with an arcsine transformation. The transformed value is equal to the arcsine of the square root of the fractional value (Sokal & Rohlf, 1981). Before the transformation, negative values (decreases in flow), were coded as zero. This coding is equivalent to assuming that no decreases in flow occurred with increasing exercise level. This assumption is reasonable for our data because no muscle showed a statistically significant decrease in flow with increasing exercise intensity. ANOVA on the transformed variables was undertaken with only the change in exercise level as a factor, because no significant variation in the transformed values existed among animals.
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Results
Individual tissues tested and their masses
Oxygen consumption
Figure 1 shows the relationship between oxygen consumption and running speed for two individuals. At speeds less than the maximum aerobic speed a linear relationship between and speed was observed. In three birds, the slope of versus speed remained constant up to (Fig. 1A). In two birds, the slope decreased slightly at speeds near the maximum aerobic speed (Fig. 1B). The mean net cost of transport for the birds was of 8.9 J kg–1 m–1, which is similar to that previously measured for running guinea fowl (Ellerby et al. 2003). The mean was 149.7 ± 3.0 ml min–1. The mean levels of oxygen consumption under conditions at which blood-flow measurements were subsequently made are shown in Table 2.
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A, data for an individual in which increased linearly with speed until was reached. B, data for an individual in which the slope of versus speed decreased slightly before was reached. Lactate and values at 0 m s–1 were obtained while the bird rested in the dark. The lines on each plot represent a linear regression equation fitted to the initial linear portion of the relation between and increasing speed.
Cardiac output and overall distribution of flow
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Cardiac output , calculated from the dilution of the injected spheres, increased linearly with the predicted from the relations between and speed for the individual birds (Fig. 2A). Cardiac output in ml min–1 was described by the following regression: + 136, r2 = 0.93. The total flow to all the leg muscles showed a similar relation to : , r2 = 0.94 (Fig. 2A). Virtually the entire increase in cardiac output with increasing exercise intensity was accounted for by the sum of the flow to the leg muscles and heart , which is described by the regression: , r2 = 0.95 (Fig. 2A). The remaining flow, – , demonstrated no significant relation with (Fig. 2B) nor did the flow to the major organs other than the heart (sum of the flow to the brain, kidneys, testes or ovaries, spleen, pancreas and digestive tract) (Fig. 2B).
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A, total cardiac output, blood flow to the leg muscles, and blood flow to the leg muscles and heart. B, total blood flow other than to the leg muscles and heart, and total blood flow to the organs excluding the heart (brain, spleen, stomach, pancreas, intestines, caecae and kidneys). The lines are linear regressions through the data (see text for equations). The regressions in B are not statistically significant.
Heart rate and stroke volume
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Cardiac output increased by 4.9-fold going from rest to 90% of . Knowing both cardiac output and heart rate (fH), we were able to calculate stroke volume (VS). Increased cardiac output was accounted for by substantial increases in both fH and VS. We measured an increase in fH from 203 ± 11 beats min–1 at rest to 451 ± 10 beats min–1 (mean ± S.E.M., n = 5, Fig. 3A) at a speed expected to produce 90% of , a factorial increase of 2.2. Values of VS increased from 1.7 ± 0.1 ml (1.1 ml kg–1) at rest to 3.8 ± 0.1 ml (2.6 ml kg–1) at the highest running speed (mean ± S.E.M., n = 5, Fig. 3B), a factorial increase of 2.2 from rest to 90%.
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A, relation between heart rate and energy expenditure; B, relation between stroke volume and energy expenditure. Energy expenditure is expressed as a fraction of the maximal oxygen consumption, . The treadmill speeds for each condition are indicated next to the plotted values. Values are shown as means ± S.E.M., n = 5.
Blood flow to the skeletal muscles
Leg muscles. After removal of the effects of inter-animal variability (see Methods) all of the leg muscles showed significant effects of exercise level on blood flow (P < 0.001 for all muscles). However, the effects of running speed on blood flow were not uniform among the muscles. The first question we asked about these variable responses was whether flow increased significantly (Contrasts Procedure, P 0.05) as the bird went from one exercise level to the next highest level. Based on this comparison, the leg muscles were divided into three categories (Fig. 4). First, 11 muscles showed significant increases in flow across all levels of exercise from rest to a fast run (Fig. 4A). Second, nine muscles showed no significant increase in flow when walking at 0.5 m s–1 compared with resting levels, but demonstrated progressive significant increases in flow at the two higher speeds (Fig. 4B). Third, four muscles had significant increases in flow at lower speeds, but showed no significant increases in flow between running at 1.5 m s–1 and > 2.4 m s–1 (Fig. 4C).
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The muscles are divided into the following categories in terms of how flow increased in relation to increasing energy expenditure: A, muscles in which there was a significant increase in mass-specific flow with each level of increased energy expenditure from rest to a fast run; B, muscles in which there was no significant change in mass-specific blood flow between rest and walking at 0.5 m s–1, but progressively increasing blood flow at higher speeds; C, muscles which had significant increases in flow at lower speeds, but showed no significant increases in flow between running at 1.5 m s–1 and > 2.4 m s–1. Statistical significance of the difference between two successive exercise levels is indicated by: *P 0.05; **P 0.01; and ***P 0.001. Abbreviations for muscle names are given in Table 1. Means ± S.E.M., n = 5.
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To further distinguish differences in the pattern of increase in blood flow, we examined the fractional contribution of each muscle to the total increase in flow to the leg muscles with increasing exercise intensity (Fig. 5). This fractional value, termed here fractional delta flow , is defined as the increase in blood flow to an individual muscle between two successive exercise levels divided by the increase in blood flow to the total mass of hind-limb muscles between the same two levels of exercise. If flow to all the muscles contributed proportionally to the increase in overall leg blood flow across speeds, one would expect values to be constant within a given muscle and to be proportional to the mass of the muscle as a fraction of the overall mass of the leg muscles. Instead we found that the pattern of increasing flow varied greatly among the muscles. The muscles could be divided into three groups based on a statistical analysis of the transformed values of (, see Methods). Group I muscles preferentially increased their blood flow when going from a rest to a walk, and had significant (P < 0.04) decreases in values as exercise intensity increased. Group II muscles had the opposite pattern of increases in blood flow, such that the values of significantly (P < 0.02) increased with exercise intensity. Finally, Group III muscles showed no significant changes in their fractional contributions to the overall increase in leg muscle blood flow across the range of exercise intensities studied.
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is the increase in flow to an individual muscle between the two levels of exercise divided by the total increase in flow to all the hind-limb muscles. The bottom axis represents the arcsine-transformed values of ; transformed value = . This transform is non-linear as indicated by the top axis, which represents the values converted to a percentage. Muscles are divided into three groups. Group I muscles show significant decreases in the transformed values of as exercise intensity increases. Group II muscles show significant increases in the transformed values of . Group III muscles show no significant change in the transformed values of across exercise levels. Statistical significance of the difference between two successive arcsine-transformed values of is indicated by: *P 0.05; **P 0.01; and ***P 0.001. Abbreviations for muscle names are defined in Table 1.
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Skeletal muscles outside the hind limb. The intercostal muscles and the abdominal wall muscle, which are associated with respiratory movements, show a pattern of flow changes with exercise level similar to that found in the hind-limb muscles (Fig. 6). Blood flow did not change between rest and walking, but then showed progressive, significant (P < 0.05) increases at higher running speeds. The pectoralis and supracoracoideus muscles showed significant (P < 0.02) effects of exercise level in the overall model. However, in the pectoralis no paired comparisons were significant with either the Contrasts test or the Games-Howell test. The supracoracoideus showed a significant (Contrasts, P < 0.02) increase in flow, at the highest running speed, but showed no significant change in flow at lower speeds.
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Mean ± S.E.M., n = 5. The mean muscle masses (± S.E.M.) for both sides are given below the name for the flight muscles. Total masses are not given for the respiratory muscles, because the entire muscle mass was not dissected. Supracor., supracoracoideus
Blood flow to the organs
Heart. At all speeds, the ventricles had the highest mass-specific blood flow of all the tissues for which we made measurements (Fig. 7). Both the atria and the ventricles showed significant effects of exercise level in the overall model (P < 0.001). However, in the paired comparisons both the atria and the ventricles only showed significant increases (P < 0.05) between 0.5 m s–1 and 1.5 m s–1.
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Mean ± S.E.M., n = 5. The mean organ masses (± S.E.M.) are given below the name of each organ. Masses for the kidneys and reproductive (repro.) organs are the sum of right and left organs. The reproductive organs are the testes in males and the ovaries and oviduct in females.
Brain and abdominal organs. Among the organs other than the heart, only the brain and proventriculus showed significant (P < 0.03) effects of exercise level on blood flow in the general linear model (Fig. 7). The mean flow to the brain increased with increasing exercise intensity, and in paired comparisons the increase from 0.5 to 1.5 m s–1 was statistically significant (P = 0.018). Mean flow to the proventriculus increased from rest to 0.5 m s–1, and then declined at higher speeds. In paired comparisons the only significant contrast (P = 0.03) was due to the increase in flow from rest to 0.5 m s–1. Flow to the remaining organs was essentially constant at all levels of exercise.
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Blood haemoglobin and lactate. The total amount of blood withdrawn during the blood-flow experiments was less than 5% of the estimated blood volume of the birds and we saw no decrement in haemoglobin concentration with the successive blood withdrawals. The birds in the present study fell into two groups in terms of their blood haemoglobin concentrations. Two mature, egg-laying females had mean blood haemoglobin concentrations of 10.6 and 10.9 g dl–1 of blood. The remaining three birds, two males, and a younger non-ovulating female had a mean haemoglobin concentration of 15.1 ± 0.2 g dl–1 of blood (mean ± S.E.M.). In the three individuals with the higher blood haemoglobin concentration, lactate levels remained low at all exercise levels (1.10 ± 0.18 mmol l–1, mean ± S.E.M., n = 12). Low lactate levels were also found in the two egg-laying females, except at the highest running speed at which lactate levels were elevated. These two females were the birds that showed a slightly reduced slope of versus speed at speeds near (Fig. 1).
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Discussion
Blood flow as a measure of energy use by active skeletal muscle
Microsphere measures of blood flow in muscle have been used in a variety of contexts, but only recently have they been used to partition energy expenditure among individual active muscles during exercise (Marsh et al. 2004). Armstrong & Laughlin (1985) examined the use of this technique as a potential measure of muscle energy use, but were cautious because they considered that factors other than local metabolic rate might play a significant role in determining blood flow. Based on the evidence outlined briefly below, we have concluded that this technique is the most reliable method available for estimating the energy use by all of the individual active skeletal muscles during aerobically supported terrestrial locomotion.
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This conclusion is based on the observation that during aerobically supported exercise the metabolic rate of active muscle is proportional to blood flow. The available evidence from in vivo studies suggests that blood flow to active skeletal muscle is controlled so that the rate of oxygen delivery is directly proportional to the metabolic rate (Delp & Laughlin, 1998; Murrant & Sarelius, 2000; Laughlin & Korzick, 2001; Boushel, 2003). Measures of total systemic flow support the correlation of muscle blood flow and muscle . Cardiac output increases linearly with in guinea fowl (Fig. 2) and other animals (Barger et al. 1956; Stenberg et al. 1967; Grubb, 1982; Grubb et al. 1983; Armstrong et al. 1987; Snyder et al. 1999). In guinea fowl all of the increase in flow during exercise is directed toward the active skeletal muscles and the heart (Fig. 2). In humans the relation of blood flow (or oxygen delivery) to has also been found to hold for skeletal muscle flow specifically (Saltin et al. 1998), as it does for leg muscle blood flow in guinea fowl (Fig. 2).
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The magnitude of blood flow cannot always be taken as an indicator of metabolic rate. Extraction of oxygen by resting muscle is lower than active muscle, and therefore the correlation of blood flow and may not hold for resting skeletal muscles either pre- or post-exercise (Bangsbo & Hellsten, 1998; Clark et al. 2000). Blood flow should also not be used as an index of metabolic rate for organs such as the intestines, stomach and kidneys, in which regulation of flow is not tied primarily to metabolic rate.
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Of course, the correlation of blood flow with energy use only holds for aerobically supported muscle activity. In the present study, we chose a maximum exercise level equal to that expected to produce a of 90% of . In 3 of the 5 birds, this level of effort was totally aerobic, as indicated by the low lactate values. In two birds, some lactate accumulated at the highest running speed (Fig. 1). In these birds 3–5% of the energy use was supported anaerobically based on the decrease in slope of versus speed at speeds approaching (Fig. 1).
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Cardiac output and oxygen delivery to the tissues
Cardiac output. Guinea fowl had a 4.9-fold increase in cardiac output between rest and 90% . The increase in cardiac output reached during exercise in these birds is not unusual. Our resting and near-maximal values of cardiac output are close to those predicted for guinea fowl based on the equations of Grubb (1983) and Bishop (1997). Precise comparisons with empirical data on the cardiac output of other avian species is difficult because the level of energy expenditure relative to is not known for any other study of cardiac output in birds. However, studies of flying pigeons and running emus that involve large multiples of resting metabolic rate report increases in cardiac output of 4.3- and 7.2-fold, respectively (Butler et al. 1977; Grubb et al. 1983).
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Although the increase of cardiac output achieved by exercising guinea fowl is not unusual, the relative contribution of stroke volume in achieving this increase (Fig. 3) is greater than has been found in most other studies of birds and mammals. Several other species of birds show little or no increase in stroke volume during treadmill exercise (Grubb, 1982; Grubb et al. 1983; Kiley et al. 1985; Fedde et al. 1989) or during wind-tunnel flights (Butler et al. 1977). The emu has a substantial increase in cardiac output during submaximal exercise (Grubb et al. 1983), but the increase in heart rate still dominated in increasing cardiac output. This pattern of predominantly heart rate-mediated changes in cardiac output is also typical of mammals (Armstrong et al. 1987; Taylor et al. 1987; Jones et al. 1989), except for elite human athletes, who exhibit a doubling of stroke volume between rest and maximal exertion in conjunction with a factorial increase in heart rate of 2.6 (Zhou et al. 2001).
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Blood flow to the viscera. The blood flow to the tissues of the guinea fowl other than the heart and working skeletal muscles remains constant as exercise intensity increases (Fig. 2). A related bird, the domestic fowl, also shows no evidence for redistribution of blood flow away from the gut during exercise (Brackenbury et al. 1990, 1993). In many other vertebrates studied to date, visceral blood flow decreases during exercise (Rowell, 1974; Laughlin & Armstrong, 1982; Manohar, 1986; Armstrong et al. 1987). In dogs the response of visceral flow to exercise is mixed (Musch et al. 1987). The lack of diversion of visceral flow in our study could reflect the fact that our birds were endurance trained. Endurance training in rats blunts, but does not eliminate, the redistribution of visceral flow (Armstrong & Laughlin, 1984). However, the lack of diversion of visceral flow in guinea fowl may also be an evolutionary consequence of their ecology. When locomotion is a regular part of daily activity, as it is in guinea fowl (Ayeni, 1982), then redistribution of flow could incur a significant cost in terms of slower digestion and impaired excretion.
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Oxygen delivery to the working skeletal muscles and heart. Birds require greater blood flow to the working muscles for a given increase in metabolic rate than do mammals. Grubb (1982) found that the slope of cardiac output versus was higher in pigeons and ducks than had been found previously for mammals and suggested that birds might have lower extraction efficiencies than mammals. The higher slope is confirmed by other studies in birds (Butler et al. 1977, 1988; Bech & Nomoto, 1982; Grubb, 1983). Birds must pump approximately 10 ml of blood for every 1 ml increase in oxygen consumed, whereas typical values for mammals are 5–7 ml of blood for each millilitre increase in oxygen use (Barger et al. 1956; Stenberg et al. 1967; Armstrong et al. 1987; Saltin et al. 1998; Snyder et al. 1999). The slope of the relation of blood flow to should be inversely proportional to the extraction efficiency of the active muscles (Wolff, 2003). We did not measure the oxygen content of the blood directly in the present study. However, using the slope of leg blood flow versus for guinea fowl and the measured haemoglobin concentration, we estimate that the extraction efficiency of the guinea fowl leg muscles was approximately 60%. Similar calculations from studies of birds in which blood oxygen content was measured directly yield estimated extraction efficiencies for avian skeletal muscle of 67–73% (Butler et al. 1977; Bech & Nomoto, 1982; Grubb, 1983). Extraction efficiencies for mammalian muscles estimated similarly typically range from 80 to more than 90% (Taylor et al. 1987). The reasons for the lower extraction efficiencies in avian muscle as compared with mammalian muscle are not known, but one factor could be the characteristics of the large nucleated red blood cells found in birds.
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Probably because of this lower extraction efficiency, the mass-specific flow to the ventricles and skeletal muscles of guinea fowl were very high when the birds were exercised at levels approaching . The values reported here for the ventricles and some skeletal muscles, e.g. the PIFL, are the highest values of mass-specific blood flow reported for muscle. The limited other data available for other avian muscles suggest that these values are not unrealistically high. In swimming ducks, Butler et al. (1988) report a flow to the heart of 3.6 ml g–1 min–1, a mean flow to the leg muscles of 3.4 ml g–1 min–1, and a maximal flow to one highly aerobic muscle of 8.6 ml min–1 under conditions eliciting increases in cardiac output of 1.7-fold. Similarly, Duchamp & Barre (1993) reported blood flow to the heart of 5–7 ml g–1 min–1 during cold exposure that elicited an increase in cardiac output of only 1.3- to 1.6-fold. Our maximal values were higher than these literature values for the ventricles (14.5 ml g–1 min–1, and some skeletal muscles, e.g. 9.6 ml g–1 min–1 for the PIFL (Fig. 4), but our peak exercise levels required an increase in cardiac output of nearly 5-fold.
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Partitioning energy use with increasing exercise intensity
Cardiac muscle and respiratory muscles. Energy use increases linearly with speed in guinea fowl as it does in other animals using terrestrial legged locomotion (Ellerby et al. 2003). Examining the distribution of blood flow allows us to partition this energy expenditure among various muscles. Approximately 90% of the increase in cardiac output with exercise in guinea fowl serves to supply blood to the working hind-limb muscles (Fig. 2), with the remaining approximately 10% providing increased flow to the coronary circulation and the respiratory muscles. The heart receives between 8 and 9% of the increase in flow, which is higher than values estimated from studies on mammals (Armstrong et al. 1987; Musch et al. 1987). The amount of blood flow distributed to the respiratory muscles of guinea fowl is quite small. We do not have precise measures of the total respiratory muscle mass in guinea fowl, but based on the increase in flow from rest to exercise that is not accounted for by flow to the heart and hind-limb muscles, the respiratory muscles receive only approximately 2% of the increase in blood flow from rest to 90% of .
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Fractional contributions of individual hind-limb muscles to total energy use. Increasing energy use (as indicated by blood flow) with increasing running speed differs greatly among the hind-limb muscles. We divided the muscles into three groups based on their fractional contributions to the total increase in flow ( values) with increasing exercise intensity. Group I muscles are muscles that preferentially used energy at the lower locomotor speeds and decreased their fractional contributions to the increase in energy use at greater speeds (Figs 5 and 8). The five muscles in Group I made up just 25% of the hind-limb muscle mass, but these muscles were responsible for 57% of the increase in energy use between rest and walking at 0.5 m s–1 (35% of ). However, the contribution of these muscles to the increase in energy use as the birds go from 63 to 90% of is only 14%. Decreasing values do not mean blood flow decreased in these muscles at high speeds (Fig. 8B), but that the further increase in total flow to the leg muscles at high speeds was due to increases in flow to other muscles. Of the muscles in Group I only the ITC showed a statistically significant increase in flow as organismal energy use was increased from 63 to 90% of . The four large muscles and five small muscles in Group II showed the opposite pattern of increases in energy use to those in Group I (Figs 5 and 8). Group II muscles make up 46% of the hind-limb muscle mass, but are responsible for only 15% of the increase in energy use as the birds go from rest to walking at 0.5 m s–1. Conversely, at high levels of exercise, the running bird is increasingly dependent on these muscles. They are responsible for 58% of the increase in energy use as metabolic rate increases from 63 to 90% of . Group III muscles increase their energy use approximately in proportion to the overall increase in metabolic rate across all exercise levels (Figs 5 and 8). They make up approximately 28% of the hind-limb muscle mass and contribute approximately the same percentage to the increase in energy use across all exercise levels.
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The muscles are divided into groups based on the statistical differences in transformed values of (Fig. 5). Group I muscles are muscles that disproportionately receive blood flow at lower running speeds and receive less of the increase in flow at higher speeds. Group II muscles are muscles that increase their contributions to the increment in flow at the higher speeds. Group III muscles maintain approximately the same contribution to the increase in flow over all increments in exercise intensity. In A the height of the bar for each muscle (or muscle group) represents the percentage of the total increase in flow between exercise levels that is accounted for by flow to that muscle. In B the height of the bars for each muscle represents the absolute flow in ml min–1. To simplify the figure, flows to the three heads of the gastrocnemius (GAST) were combined, as were the flows to some small muscles with small individual contributions to the overall flow; Sm. Mus. II are ITCR, CFP, ILPR, CFC, Amb.; Sm. Mus. III are ISF, OM, DE. Abbreviations for individual muscles are given in Table 1.
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Our data show large differences in energy use by agonistic muscles within and across running speeds in guinea fowl. This suggests that the common practice of distributing work or force estimated from external measures across a group of agonists based solely on the mass or cross-sectional area of the muscles should be viewed with caution. For example, among the ankle extensors in the guinea fowl, the Group I digital flexor and FL muscles are approximately the same size as the Group III gastrocnemius muscles. However, largely due to the substantial contributions of the digital flexors, the Group I muscles used more than 70% of the energy consumed by ankle extensors when guinea fowl walked at 0.5 m s–1. Among the hip extensors the Group I muscles (PIFM, PIFL and ITC) together have a mass of only 36% of the Group II and III hip extensors (ILPO, FCLP, FCLA, posterior IF, CFP and CFC). However, the Group I hip extensors consumed 2.7 times the energy during walking as did the Group II and III hip extensors. Because of the opposite patterns of increasing energy use with speed in Group I and II muscles, the distribution of total energy use among the agonistic muscle groups shifts with increasing speed. At the highest speed tested, the distribution of energy use approximates the distribution of mass among these muscles.
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The differences in the pattern of increasing blood flow with increasing exercise intensity among the hind-limb muscles are correlated with probable differences in fibre type. Other studies of muscle blood flow have found that at low exercise intensities increases in blood flow are seen preferentially in regions of muscles with slow-oxidative fibres. Areas rich in fast-oxidative fibres show increases in flow at intermediate exercise intensities, and areas where fast-glycolytic fibres predominate show the greatest increases in flow at the most intense levels of aerobic exercise (Laughlin & Armstrong, 1985). In guinea fowl, blood flow to the puboischiofemoralis medialis muscle (PIFM) increases 16-fold between rest and the slowest speed tested and shows only a modest increase in flow with increasing running speed (Fig. 4). In the domestic fowl and quail, which are members of the same family of birds, this muscle has the highest proportion of slow fibres of any thigh muscle (Suzuki & Tamate, 1979; Suzuki et al. 1985; Crow & Stockdale, 1986). The PIFL, another group I muscle, is made up almost exclusively of fast-oxidative fibres, and is lacking in fast-glycolytic fibres in the domestic fowl (Suzuki et al. 1985). The flexor cruris lateralis pars pelvica (FCLP) and the posterior part of the iliotibialis (ILPO), which are Group II muscles, show a very different pattern of increasing blood flow. Flow to these muscles does not increase significantly relative to rest when the birds were walking at 0.5 m s–1 but increases greatly at higher speeds (Figs 5 and 8). The association of these muscles with higher-speed locomotion suggests that they should mainly be composed of fast oxidative and glycolytic muscle fibres, with few slow-oxidative fibres. In the domestic fowl the FCLP is entirely lacking in slow-oxidative fibres, containing approximately 30% fast-oxidative-glycolytic fibres and 70% fast-glycolytic fibres, and the ILPO has a similar composition (Suzuki et al. 1985). No data are available for the fibre composition of the FCLA. Group III muscles, which show progressive increases in values with speed, are predicted to contain a mix of fibre types ranging from slow oxidative to fast glycolytic that would be recruited across the range of exercise intensities from walking to running at near . This fibre composition appears to be the case for the muscles in this group for which fibre type data are available on related species of running birds. The IC, FCM and LG all have a mix of slow-oxidative, fast-oxidative-glycolytic and fast-glycolytic fibre types (Kiessling, 1977; Suzuki et al. 1985).
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Swing versus stance. Our primary interest in studying blood flow during running is to be able to relate the energetics of muscle use to the mechanical function of the individual muscles used. Most studies attempting to integrate muscle function and energy use during running have inferred the relations between these variables from externally measurable biomechanics and organismal energy use. Knowing the distribution of energy use among the individual muscles should allow us to better understand information on the in vivo function of the muscles. For example, Marsh et al. (2004) used blood flow data on guinea fowl to conclude that swinging the limbs consumes approximately a quarter of the energy expended during locomotion, independent of running speed. Across the three walking and running speeds tested, the individual muscles differ in the fraction of total hind-limb flow that they receive, but the sums of the flows to the swing and stance phase muscles represent remarkably constant fractions of the total flow (Marsh et al. 2004).
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Our estimate from the proportion of blood flow to the swing-phase muscles can be combined with data on the metabolic cost of running (Ellerby et al. 2003) and estimates of the segmental mechanical work required to swing the limbs in guinea fowl (Fedak et al. 1982) to calculate the mechanical efficiency of the swing-phase muscles. These estimates of efficiency range from approximately 6% when the birds walk at 0.5 m s–1 to approximately 13% at the highest running speed. These values for efficiency, particularly the value for walking, are considerably lower than the maximal values cited for aerobically functioning muscle of approximately 25%. One likely explanation for the low efficiency is that the swing phase muscles are unlikely to purely be doing positive work. At the start of the swing phase the direction of motion of the limb is reversed. Work must be absorbed to decelerate the limb; subsequently work is done on the limb to lift and accelerate the limb forwards. Muscles are also probably actively stretched to absorb work and act as brakes to decelerate the limb before the foot touches the ground. However, the possibility also exists that more mechanical work is done than that calculated by Fedak et al. (1982). Better estimates of work at the joints in the limb might be obtained from inverse dynamics calculations on birds under similar experimental conditions to those employed in our blood-flow studies.
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Supporting body weight. A complete analysis of the relations between the energy use of muscles and their mechanical functions is not possible with the current state of knowledge, but some observations can be made that indicate the usefulness of having an estimate of individual energy use. First, our data indicate that the cost of supporting body weight at the knee in guinea fowl may be less than the cost at the hip or the ankle, particularly during walking. Two large muscles active during stance serve as knee extensors, the FT and the ILPO. The ILPO is also a hip extensor and thus can also contribute to supporting body weight at the hip. The average moment arm of the ILPO at the hip is larger than at the knee. The FT and ILPO consume just 11% of the energy used by the hind limb during walking. At higher speeds the energy use by the FT and ILPO increases because of increases in the fraction of flow distributed to the ILPO. These two muscles consume 22 and 24% of the hind-limb energy use when the exercise level is 63 and 90% of , respectively. The actual cost for weight support during stance is likely to be less than these values indicate for several reasons. First, the FT is also active during swing phase (Marsh et al. 2004). Second, the FT and the ILPO probably also perform some positive work during stance phase. Finally, a portion of the force production of these muscles must also be used to counteract the knee flexors that are co-activated (see below). Muscles with hip extensor moments consume between 39 and 42% total energy used by the locomotor muscles, and the ankle extensors consume between 27 and 32%. Of course, these muscles probably also perform functions other than just weight support. For example, the FL in turkeys has been shown to produce significant amounts of work during level running (Gabaldon et al. 2004).
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Performing work. Our data indicate that substantial energy is used by muscles performing positive work in running guinea fowl. Previous investigators have suggested that supporting body weight is the main function of the leg muscles and that this could be done most economically by muscles operating nearly isometrically as ‘struts’ tensioning the elastic tendons that absorb and release the mechanical work required in every stride (Kram & Taylor, 1990; Taylor, 1994; Roberts et al. 1997). Some muscles have been found to operate in this manner, including the LG in running birds (Roberts et al. 1997; Daley & Biewener, 2003) and the ankle extensors of hopping wallabies (Biewener et al. 1998). These lower limb muscles are suited to this function in that they have relatively short fascicles and long tendons. However, many of the thigh muscles of running mammals and birds have relatively long fascicles and short tendons. This architectural design requires the active fascicles in these muscles to track the movements of the joints. In the guinea fowl many of these muscles are predicted to shorten while active based on joint angles and EMG activity (Gatesy, 1989), and data from our laboratory confirm this pattern of activity using sonomicrometry and EMG recordings (Buchanan, 1999; R.L. Marsh, D.J. Ellerby, J.A. Carr & T.A. Hoogendyk, unpublished observations). Additionally, some of the muscles with architectures similar to the LG may also do considerable work in level running, e.g. the FL (Gabaldon et al. 2004). Our preliminary estimate is that in level running guinea fowl at least 45% of the energy is used by leg muscles performing positive work, and this estimate may be considerably higher if short-fascicled muscles other than the FL also do positive work.
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Stability and co-contraction. Recent studies of legged terrestrial locomotion have emphasized the importance of stability and its possible costs (Seipel et al. 2004; Donelan et al. 2004). Because the mechanical functions of muscle that lead to stability have not been unequivocally identified, we cannot partition the cost of stability using our blood-flow data and available data on in vivo function. However, we can put a preliminary estimate on the cost of co-contraction, which has been suggested to be one important aspect of stabilizing certain joints (Wagner & Blickhan, 2003). In the guinea fowl, co-contraction is a conspicuous feature of muscles operating around the knee. The knee extensors, the FT and the ILPO, are simultaneously active with knee flexors, the FCLP, FCM, IF, IG and LG. We estimate that these co-contracting muscles consume between 15 and 30% of the total energy used by hind-limb muscles over the range of speeds studied. (This sum was done after reducing the contributions of the FT and IF by 50% to account for their swing phase activity and reducing the contribution of the FCLP by 50% to account for its activity in late swing as a pure hip extensor in combination with the FCLA.) The cost of co-contraction may be higher because portions of other lower leg muscles also act as knee extensors or flexors including the MG, the FL and several of the digital flexors. Of course it could also be argued that because many of these muscles cross multiple joints they are also consuming energy to carry out other functions. Nevertheless, our data illustrate the potential cost of stabilizing the knee during bipedal running in birds.
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Conclusions
First, overall blood flow to the leg muscles in running guinea fowl increased in proportion to metabolic rate. This result and considerable data from the literature are consistent with using blood flow as an effective indicator of energy expenditure by active skeletal muscles during aerobically supported locomotion. Measuring blood flow seems to be the only practical way of estimating the energy consumption of individual skeletal muscles in freely locomoting animals in which many muscles are active.
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Second, guinea fowl were unusual in their overall cardiovascular response to exercise in two respects. The increase in cardiac output associated with exercise was achieved by equal factorial changes in stroke volume and heart rate, rather than the mainly heart rate-mediated changes shown in most other birds and mammals. Also, blood flow to the viscera was maintained during exercise, in contrast to the redistribution of flow to the active muscles measured in all other species for which data are available.
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Third, when the birds exercised at 90% of , the values of mass-specific blood flow to the heart and some of the hind-limb muscles were the largest reported for any muscles. However, they are consistent with the limited data available for other birds exercising at lower aerobic intensities. The large flows are probably due to the demanding level of exercise performed and the fact that birds apparently have lower extraction efficiencies in their muscles; thus they require greater amounts of muscle blood flow per amount of oxygen consumed.
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Fourth, approximately 90% of the increased cardiac output during exercise was directed to the hind-limb muscles. The patterns of increasing flow, and by inference energy use, with increasing speed varied greatly between different muscles. The muscles were divided into three groups depending on whether their blood flow increased preferentially at low speeds, high speeds or equally across all speeds. As expected from previous work in mammals, these patterns were correlated with the likely fibre type distribution of the muscles (based on information on related species of birds).
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Fifth, the differences in energy use we have demonstrated within groups of agonistic muscles have important implications for biomechanical models that must specify the distribution of activation among agonists. Knowing the distribution of energy use among muscles with different actions will allow the evaluation of future inverse dynamics calculations that predict the force, work, and power required at each joint.
Six, our data, when combined with available data on the mechanical functions of individual muscles, enabled the energetic costs of various mechanical functions to be estimated from the partitioning of blood flow among the muscles. The estimated energetic costs of swinging the limbs were approximately a quarter the total energetic costs of locomotion, regardless of speed. Co-contraction of muscle agonists at the knee accounted for approximately 15–30% of the energy costs of locomotion. Substantial energy costs were also suggested to be associated with the production of positive work. This information contrasts with one of the prevailing views of terrestrial locomotor energetics in which the energetic costs of force production during stance are thought to account for most of the energy use.
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