Blackwell Publishing LtdOxford, UKBIJBiological Journal of the Linnean Society0024-4066© 2007 The Linnean Society of London? 2007
921
6376
Original Article
RESTING METABOLIC RATE IN GROWING AMNIOTES
L. MONTES
ET AL
.
Biological Journal of the Linnean Society, 2007, 92, 63–76. With 5 figures
Relationships between bone growth rate, body mass
and resting metabolic rate in growing amniotes:
a phylogenetic approach
LAËTITIA MONTES1, NATHALIE LE ROY1, MARTINE PERRET2,
VIVIAN DE BUFFRENIL1, JACQUES CASTANET1 and JORGE CUBO1*
1
Ostéohistologie Comparée (UMR CNRS 7179), Université Pierre & Marie Curie, 2, place Jussieu, case
7077, F-75005 Paris, France
2
Laboratoire d’Ecologie Générale (UMR CNRS 7179), Muséum National d’Histoire Naturelle, 4, avenue
du Petit Château, F-91800 Brunoy, France
Received 4 July 2006; accepted for publication 21 October 2006
We explored the factors that explain the variation in resting metabolic rates (RMR) in growing amniotes by using the
phylogenetic comparative method. For this, we measured raw RMR (mL O2 h−1), body mass, body mass growth rate,
and periosteal bone growth rate in a sample of 44 growing individuals belonging to 13 species of amniotes. We performed variation partitioning analyses, which showed that phylogeny explains a significant fraction of the variation
of mass-specific RMR (mL O2 h−1 g−1), and that the cost of growth is much higher than the cost of maintenance. Moreover, we tested the hypothesis of the independence of energy allocation, and found that maintenance metabolism and
growth rates are not significantly related. Finally, we calculated the statistical parameters of the relationship
between geometry-corrected RMR (mL O2 h−1 g−0.67) and bone growth rate. This relationship could potentially be used
in palaeobiology to infer RMR from bone tissue samples of fossil species by assuming Amprino’s rule (according to
which bone tissue types reflect bone growth rates). These estimates would be especially interesting for Mesozoic nonavian theropod dinosaurs and Permian and Triassic therapsids to investigate, respectively, the origin of avian and
mammalian endothermy. © 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007,
92, 63–76.
ADDITIONAL KEYWORDS: Aves – bone tissue – Chelonia – Crocodilia – independent contrasts – Lepidosauria – Mammalia – phylogenetic comparative method – variation partitioning.
INTRODUCTION
The mass-specific resting metabolic rate (RMR) of an
individual (mL O2 h−1 g−1) can be partitioned among
three components in amniotes: maintenance, growth
and reproduction (Gadgil & Bossert, 1970; Wieser,
1994). Many differences in metabolic rates exist
between ectothermic (chelonians, lizards, and crocodiles) and endothermic (mammals and birds)
amniotes. For example, whereas adult ectothermic
amniotes may have all three energy components
because they maintain residual growth after reaching
*Corresponding author. E-mail: [email protected]
sexual maturity, adult endothermic amniotes may
have only two energy components (maintenance and
reproduction) because their growth becomes negligible
after sexual maturity (Nagy, 2000). Moreover, to the
extent that thermoregulation in ectotherms relies, at
least in part, on ethological control of temperature
(e.g. heliothermy), it should be less important in terms
of energy cost than in endotherms. Conversely, energy
expenditure for thermoregulation by heat production
is quite heavy in endotherms (Nagy, 2000). The metabolic rate is approximately one order of magnitude
higher in endothermic amniotes than in ectothermic
ones of similar body mass (Wieser, 1994). Consequently, analyses of metabolic rate variations have
traditionally been performed separately in ectother-
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
63
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L. MONTES ET AL.
mic (Bennett & Dawson, 1976) and endothermic species (Nagy, 1987; Weathers & Siegel, 1995; White &
Seymour, 2005).
It has also been shown that the ratio of somatic
production to total expenditures is nearly identical
in ectothermic vertebrates and in mammals (Wieser,
1985). Consequently, ‘in both endo- and ectotherms
maintenance and growth rates show proportional representation’ (Wieser, 1994) in such a way that the
analysis of the variation of metabolic rates in samples,
including both ectothermic and endothermic species
(Waltari & Edwards, 2002), is entirely justified. In this
context, the use of the phylogenetic comparative
method (an approach which emphasizes the importance of evolutionary history in explaining current
function; Garland, Bennett & Rezende, 2005) is
necessary to avoid biases linked to non-independent
values. The present study falls within this framework
and is aimed to explore the factors that explain the
variation of RMR in growing amniotes. The study has
four main objectives.
First, we tested the presence of a phylogenetic signal in the variation of resting metabolic rate. Among
the methods available for testing phylogenetic signal
in continuous characters (Cubo et al., 2005), we chose
variation partitioning analysis including the phylogeny as an explanatory factor (Desdevises et al., 2003)
because it supplies a clear visual representation of the
portion of the variation of resting metabolic rate
explained by functional factors, the portion explained
by phylogeny, and their overlap.
Second, the relationships between the two functional variables considered here (cost of growth and
cost of maintenance) have been debated (Konarzewski, 1995; Steyermark, 2002). Three competing
hypotheses have been proposed: (1) The high rates of
protein synthesis and degradation (protein turnover)
linked to the building of new tissues at high growth
rates increase maintenance metabolism (Else & Hulbert, 1985; Karasov & Diamond, 1985). According to
this hypothesis, we could predict that organisms with
higher growth rates would have higher maintenance
metabolism. (2) The quantity of available energy for a
given organism is limited and, in consequence, there is
a trade-off between maintenance metabolism and
growth rate (Wieser, 1994; Steyermark, 2002). According to this hypothesis, organisms with higher growth
rates should have lower maintenance metabolism. (3)
The amounts of energy allocated to growth and maintenance are independent of each other (Dunn, 1980).
A second objective of our study is to test these three
competing hypotheses.
Third, the cost of maintenance strongly depends on
the size of an organism: for a given thermometabolic
regime (i.e. ectothermic or endothermic), 1 g of a small
animal spends more energy than 1 g of a large animal
(Withers, 1992). Two hypotheses have been proposed
to explain the scaling of raw RMR (mL O2 h−1) in
amniotes (for a precise definition of this variable, see
Material and methods). According to the geometric
similarity hypothesis, a two-fold increase in length is
linked to a four-fold increase in surface and an eightfold increase in volume in geometrically similar animals. Thus, within each clade (e.g. mammals), small
animals may have higher surface to volume ratios and
greater mass-specific heat loss through their surfaces
than bigger ones (assuming the thermic isolation provided by the integumentary structures is independent
from size within a given clade). Accordingly, we can
predict that raw RMR, like the surface to volume
ratio, may scale to body mass raised to the power of
0.67 (Andrews & Pough, 1985; Bennett & Harvey,
1987; Withers, 1992:; White & Seymour, 2005).
According to the additive scaling hypothesis, however,
the exponent of the relationship between raw RMR
and body mass may be the additive result of two
factors, the scaling of the surface to volume ratio
(∝ mass0.67) and a mass effect (∝ mass1.0), which
implies that raw RMR ∝ mass0.75 (Withers, 1992).
Therefore, a third objective of this paper is to study
the allometry of raw RMR by using independent contrasts. Most of the preceding studies dealing with this
subject were performed either on ectothermic species
or in endothermic ones because it was considered that
metabolic rates of these groups were too different to be
analysed together. Independent contrasts analysis
deals with evolutionary changes linked to each dichotomy of the phylogenetic tree (e.g. the change associated to the split between crocodiles and birds) and
allows the inclusion of both ectothermic and endothermic species.
Finally, the evolution of physiological characteristics of amniotes has been an important focus for
researches during the last three decades. Notably, the
issue of thermometabolic evolution among amniotes,
from the presumably ectothermic condition in Paleozoic early amniotes to the derived endothermic condition of extant birds and mammals, has been a matter
of special interest (Schweitzer & Marshall, 2001).
Comparative histological studies of bone tissue among
extant and extinct amniotes have suggested a rather
early shift from ecto- to endothermic physiologies
among some Mesozoic Synapsids (Therapsids) and
Diapsids (Archosaurs), leading, respectively, to mammalian and avian physiological conditions (de Ricqlès,
1978). Nevertheless, the issue remained controversial
because the methodology mostly relied on comparative
qualitative approaches of the possible relationships
between bone tissue structure, growth rates, and metabolic rates (de Ricqlès, Padian & Horner, 2001). In
the present study, we take advantage of new quantitative methodologies to test the presumed relationship
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
RESTING METABOLIC RATE IN GROWING AMNIOTES
65
Table 1. Origin and captive conditions [temperature; photoperiod light/dark (h); food] of the young amniotes
Species
N
Origin
Captive conditions
Microcebus murinus (JF Miller, 1777)
4
Breeding, Brunoy, France
Cavia porcellus (Linnaeus, 1758)
3
Breeding, France
Mus musculus Linnaeus, 1758
4
Breeding, Brunoy, France
Trachemys scripta (Schoepff, 1792)
5
Breeding, Orsay, France
Pelodiscus sinensis (Wiegmann, 1834)
3
Breeding, Corsica, France
Macrochelodina rugosa Ogilby, 1890
2
Ranching in Indonesia
Lacerta vivipara Jacquin, 1787
3
Capture, Villefort, France
Podarcis muralis (Laurenti, 1768)
3
Capture, Villefort, France
Varanus exanthematicus (Bosc, 1792)
3
Ranching in Togo
Varanus niloticus (Linnaeus, 1766)
2
Ranching in Togo
Crocodylus niloticus Laurenti, 1768
3
Breeding, Pierrelatte, France
Anas platyrhynchos Linnaeus, 1758
4
Breeding, Theillay, France
Gallus gallus (Linnaeus, 1758)
5
Breeding, France
22 °C, 14 : 10 h
Maternal milk
23 °C, 14 : 10 h
Maternal milk
22 °C, 12 : 12 h
Maternal milk
28 °C, 12 : 12 h
Pellets for carnivorous turtles
28 °C, 12 : 12 h
Pellets for carnivorous turtles
28 °C, 12 : 12 h
Pellets for carnivorous turtles
25 °C, 14 : 10 h
Acheta domestica larvae
25 °C, 14 : 10 h
Acheta domestica larvae
25–40 °C, 12 : 12 h
Tenebrio molitor larvae
25–40 °C, 12 : 12 h
Tenebrio molitor larvae
28 °C, 12 : 12 h
Beef meat, fish
22 °C, 12 : 12 h
Wheat pellets for chicken
22 °C, 12 : 12 h
Wheat pellets for chicken
between bone growth rate and RMR, as a first step to
performing palaeobiological estimations.
MATERIAL AND METHODS
BIOLOGICAL
MATERIAL
This comparative study is based on 44 growing individuals belonging to 13 species of amniotes. During
the experiments, these specimens were maintained
in controlled conditions appropriate to each species
(Table 1).
RESTING
METABOLIC RATE
The basal metabolic rate (BMR) has been defined for
adult endothermic amniotes as the minimum rate of
energy expenditure measured under thermoneutral
and postabsorptive conditions in the inactive phase of
the daily cycle (Daan, Masman & Groenewold, 1990).
The equivalent variable for adult ectothermic
amniotes is the standard metabolic rate (SMR), and
is measured at a given temperature within the animal’s range of activity (Lewis & Gatten, 1985). In the
present study, we deal with growing individuals of
both endothermic and ectothermic amniotes, so nei-
ther basal nor standard metabolic rates could be
used. Instead, we measured the resting metabolic
rate (RMR), which is defined as the minimum rate of
energy expenditure under postabsorptive conditions
during the period of normal activity of the daily cycle
(Andrews & Pough, 1985). The estimation of RMR
relies on the measurement of the volume of dioxygen
consumed per time unit (mL O2 h−1). Our methodology assumes that energy production related to the
consumption of a given volume of oxygen is approximately constant, a condition currently accepted
(Schmidt-Nielsen, 1997) in spite of the fact that the
species we studied have quite different diets (carnivorous, herbivorous, granivorous). Oxygen consumption was measured by a closed-circuit respirometer,
following a method previously described by Perret,
Aujard & Vannier (1998). After being weighed to the
nearest 0.1 g, specimens were placed in opaque respiratory chambers of different volumes suited to their
sizes (0.2–2.0 L). RMR was measured in endothermic
species under thermoneutral conditions (in which
they do not expend energy in regulating their body
temperature) and in ectothermic ones at a given typical temperature within their range of activity. Ideally, we should have measured RMR at the typical
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
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L. MONTES ET AL.
Table 2. Results of the empirical measurements of the variables of interest in our sample of growing amniotes
(means ± standard deviations)
Species
Body mass (g)
RMR (mL O2 h−1)
BoneGR (µm day−1)
MassGR (g day−1)
N
Microcebus murinus
Cavia porcellus
Mus musculus
Trachemys scripta
Pelodiscus sinensis
Macrochelodina rugosa
Lacerta vivipara
Podarcis muralis
Varanus exanthematicus
Varanus niloticus
Crocodylus niloticus
Anas platyrhynchos
Gallus gallus
14.60 ± 4.70
100.00 ± 7.21
5.08 ± 0.22
16.13 ± 3.56
5.77 ± 0.65
23.85 ± 7.28
0.50 ± 0.01
1.08 ± 0.18
46.00 ± 8.00
32.50 ± 4.95
215.33 ± 23.18
109.75 ± 3.30
90.00 ± 7.07
9.20 ± 2.57
76.10 ± 5.62
5.07 ± 0.08
0.70 ± 0.20
0.27 ± 0.06
0.73 ± 0.11
0.07 ± 0.05
0.08 ± 0.05
2.03 ± 0.41
3.11 ± 1.96
11.50 ± 7.70
258.12 ± 71.81
177.48 ± 31.05
5.79 ± 0.36
21.83 ± 2.09
4.05 ± 0.87
1.06 ± 0.35
0.39 ± 0.13
0.51 ± 0.21
0.22 ± 0.12
0.15 ± 0.09
1.02 ± 0.46
0.80 ± 0.07
2.48 ± 1.09
29.72 ± 6.90
47.01 ± 13.74
0.944 ± 0.064
5.587 ± 0.182
0.595 ± 0.010
0.337 ± 0.109
0.066 ± 0.021
0.001 ± 0.001
0.004 ± 0.001
0.009 ± 0.003
0.341 ± 0.217
0.440
1.820 ± 0.285
9.050 ± 2.891
4.450 ± 2.655
4
3
4
5
3
2
3
3
3
2
3
4
5
For a description of the variables, see Material and methods.
temperature preference of each ectothermic species.
However, it was not possible to determine these specific temperature preferences for feasibility reasons.
Instead, the respiratory chamber was held at a controlled ambient temperature of 25 ± 0.1 °C for all
ectothermic species (chelonians, lizards, and crocodiles) and precocial endothermic ones (birds and
Cavia porcellus). For altricial endothermic species
(Mus musculus and Microcebus murinus), the ambient temperature was maintained at 35 ± 0.1 °C to
simulate the temperature of the nest. After 30 min of
acclimation under constant air flow, the chamber was
closed for a duration that depended on the species.
The volume of O2 consumed by each animal was calculated from initial and final concentrations of O 2 in
the chamber. Measurements were made using a paramagnetic gas analyser (Analyser 570A, Servomex
Ltd) routinely calibrated with N 2 and atmospheric air
assuming 21.00% O2. All individuals were deprived of
food for several hours before the measurements,
except young mammals that cannot be separated for
a long time from their mothers. We assumed, then,
that the measured RMR does not include the cost of
either thermoregulation (see above) or activity (locomotion or digestion). For each species, measurements
were repeated over 4 days at the same time in their
daily cycle (during daytime). The values used in the
comparative study (Table 2) are, for each species, an
average of the minimal RMR measured for each individual. To allow comparisons among species, we followed Konarzewski (1995) and examined a single
phase, rather than the whole period, of postembryonic development: RMR was systematically recorded
during the phase of rapid growth.
Raw RMR (mL O2 h−1) was used as an indicator of
the ‘whole’ energetic expenditures of the organism.
Mass-specific RMR (mL O2 h−1 g−1) was used as an
indicator of the energetic expenditures by mass unit.
Two different expressions have been proposed to correct data for the effect of body mass on the massspecific RMR: first, the mass-independent RMR
(mL O2 h−1 g–b, where ‘b’ is the allometric exponent of
raw RMR versus body mass) and, second, the geometry-corrected RMR (mL O2 h−1 g−0.67, where ‘0.67’ is the
allometric exponent of the ratio surface to volume versus body mass for geometrically similar organisms).
This last correction assumes that the effect of body
mass on metabolic rate is mediated by the fact that
both the surface to volume ratio and the caloric loss
per mass unit decrease as body mass increases (Withers, 1992; White & Seymour, 2005). The allometric
exponent ‘b’ of the expression corresponding to massindependent RMR (mL O2 h−1 g–b) could not be used
here because the body mass effect in our sample of
growing specimens is a mixture of ontogenetic allometry and interspecific allometry. Therefore, we used
the geometry-corrected RMR (mL O2 h−1 g−0.67) as an
indicator of size-independent energetic expenditures
by mass unit.
BONE
GROWTH RATE
(BONEGR) AND BODY
(MASSGR)
MASS
GROWTH RATE
BoneGR and MassGR were used in this study as indicators of the cost of growth. The periosteal BoneGR of
the individuals was quantified using in vivo fluorescent labelling (Castanet et al., 1996, 2000; de Margerie, Cubo & Castanet, 2002; de Margerie et al.,
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
RESTING METABOLIC RATE IN GROWING AMNIOTES
67
2004) during the phase of rapid growth. Solutions
corresponding to 80 mg kg−1 of animal fresh weight
for xylenol-orange (XO), and 40 mg kg−1 for fluoresceine (DCAF) were injected intraperitoneally to the
animals at different ages. These fluorescent dyes
specifically colour the mineralized zone of growing
bone tissue, in deep orange for XO, and in green for
DCAF (Fig. 1). Animals were euthanized after a given
time interval, and the left tibia was removed from
each individual. These bones were dehydrated in
graded ethanol and defatted in acetone and trichloroethylene before being embedded in a polyester
resin (Matrajt et al., 1967). Transverse sections
100 ± 10 µm thick were made at the diaphyseal level
using a diamond-tipped circular saw. Each thin section was grownd and polished before being mounted
on a slide. They were observed under fluorescent light
(Zeiss Axiovert 35), and digitalized through a camera
(Olympus). BoneGRs were calculated through pictorial analysis (Photoshop 7.0 on Mac OS X), using the
distance either between two consecutive circular fluorescent labels or between the last label and the bone
periphery, divided by the time elapsed between two
labels (Fig. 1). Although we labelled animals several
times, when possible, we considered only the labels
that corresponded to the period during which the
resting metabolic rate was measured. MassGR was
calculated as the increase in body mass during the
period when BoneGR was calculated, divided by the
time elapsed.
THE
REFERENCE PHYLOGENY
The phylogeny (topology and divergence times) of the
13 species of amniotes used in this study was compiled
from the literature (Fig. 2). The topology for Chelonia
was compiled from Gaffney & Meylan (1988). In
our sample, Trachemys (Emydidae) and Pelodiscus
(Trionychoidea) are sister groups, and this clade is the
sister group of Macrochelodina (Pleurodira). For the
squamates, the topology was compiled from Estes
(1982), Estes, de Queiroz & Gauthier (1988), Rieppel
(1988) and Caldwell (1999). Although the placement of
chelonians is still controversial (Rieppel & Reisz,
1999; Rieppel, 1999; Zardoya & Meyer, 2001), we considered them an sister-group of Diapsida as numerous
palaeontological studies have argued (Laurin & Reisz,
1995; Lee, 2001).
The divergence time between mammals and sauropsids (310 Myr) was taken from Hedges et al. (1996)
and Kumar & Hedges (1998); for a debate on this time
estimate, see Graur & Martin (2004) and Hedges &
Kumar (2004). Divergence time between lepidosaurs
and crocodilians was taken from Reisz & Müller
(2004), and those for the dichotomies between crocodiles and birds, galliformes and anseriformes, rodents
Figure 1. Portions of mid-shaft cross-sections of the tibiae
of different animals from our sample, on which periosteal
labels are shown. A, Anas platyrhynchos: (1) fluoresceine
injection 9 days after hatching and (2) xylenol-orange injection 14 days after hatching. B, Crocodylus niloticus: (1)
fluoresceine injection 2 weeks after hatching and (2) fluoresceine injection 12 weeks after hatching. C, Lacerta vivipara: (1) fluoresceine injection and (2) bone periphery
9 weeks after labelling. Scale bar = 200 µm.
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
68
L. MONTES ET AL.
Figure 2. Phylogenetic relationships among the species analysed in the present study. Divergence times in millions of
years and the names of the clades are given for each node. For the sources of the topology and divergence times, see text.
and primates, and basal divergences among rodents,
are from Kumar & Hedges (1998).
STATISTICAL
METHODS
The use of classical regression methods requires that
the values of each variable be independent of each
other (Felsenstein, 1985; Harvey & Pagel, 1991; Garland, Harvey & Ives, 1992) but, in an interspecific
study, the phylogenetic relationships between species
imply that these values are non-independent. To overcome this problem, we used two complementary methods: variation partitioning including the phylogeny as
an explanatory factor (Desdevises et al., 2003), and
phylogenetically independent contrasts (Felsenstein,
1985). We transformed data into a log 10 scale because
an evolutionary increase of, for example, 100 g is much
more likely in a lineage of large animals than in a lineage of small ones. This log10 transformation of data
makes the assumption that different lineages are
equally likely to make relative proportional changes,
and this is more realistic.
The method of variation partitioning including the
phylogeny as an explanatory factor (Desdevises et al.,
2003) allows an assessment of the variation of RMR:
(1) explained exclusively by phylogeny (expressed in
the form of principal coordinates); (2) explained exclusively by function (the cost of growth and the cost of
maintenance); or (3) explained by the overlap of these
two sets of independent variables. This method uses
multiple regressions, which are tested for statistical
significance by means of permutations (N = 999) using
the computer program ‘Permute!’ (distributed by P.
Casgrain).
Two indicators of the cost of growth (log 10 BoneGR
and log10 MassGR) and one indicator of the cost of
maintenance (log10 Body Mass) were taken a priori.
Multiple regression using a forward selection procedure was used to optimize the set of independent
functional variables that significantly contribute to
explain the variation of the dependent variable. Log10
BoneGR (as an indicator of the cost of growth) and
log10 Body Mass (as an indicator of the cost of maintenance) were selected and included in the set of independent functional variables. Log10 MassGR was not
added to the model because this independent variable
is likely to be correlated with the selected log 10
BoneGR.
The phylogeny (Fig. 2) was expressed in the form of
principal coordinates, which were computed from the
phylogenetic distance matrix using principal coordinate analysis (Diniz-Filho, de Sant′ Ana & Bini, 1998)
by using the computer software ‘R’ (Casgrain & Legendre, 2004). The principal coordinates PC1, PC2 and
PC3, representing 76.05% of the phylogenetic variance, were selected by reference to a broken stick
model (Diniz-Filho et al., 1998).
Although we could have obtained ‘phylogenetically
independent slopes’ by using the residuals of the
regression of traits on the selected phylogenetic principal coordinates (PC1, PC2 and PC3), we used phylogenetically independent contrasts (Felsenstein, 1985)
because we were also interested on the analysis of the
evolutionary change linked to each dichotomy of the
phylogenetic tree. For this, we used the computer program CAIC (Purvis & Rambaut, 1995). With respect to
the calculation principles, we consider a fully bifurcating phylogenetic tree with N extant species, and N − 1
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
RESTING METABOLIC RATE IN GROWING AMNIOTES
nodes (dichotomies). For each node, the value of a
character is estimated as the average value of the
character in the two following taxa, weighted by
branch lengths. These dichotomies can involve: (1) two
terminal species (e.g. Gallus gallus and Anas platyrhynchos: contrast ‘AVES’ in Fig. 2); (2) a terminal species and the last common ancestor of a clade (e.g.
Crocodylus niloticus and the last common ancestor of
Aves: contrast ‘ARCHOSAURIA’ in Fig. 2); or (3) the
last common ancestors of two clades (e.g. Lepidosauria
and Archosauria: contrast ‘DIAPSIDA’ in Fig. 2). Contrasts are calculated as the difference between character states in the two taxa of each dichotomy, also
weighted by branch lengths (they are standardized).
We followed Garland et al. (1992) and, for each analysis, we tested: (1) Evolutionary assumption: independent contrasts analyses assume a Brownian model of
character evolution. This model predicts that the absolute values of standardized contrasts should be independent of the estimated values of the character at the
nodes at which the contrasts were taken. Regressions
of the absolute values of standardized contrasts on the
estimated nodal values did not have slopes significantly different from zero. (2) Statistical assumption:
regression models assume that variance around the
regression line is the same for all values of the predictor variable (homoscedasticity). Regressions of the
absolute values of standardized contrasts on their
standard deviations did not have slopes significantly
different from zero. Finally, we performed a leastsquares linear regression through the origin of contrasts for the dependent variable on contrasts for the
independent variable.
RESULTS
PARTITIONING THE VARIATION OF MASS-SPECIFIC
RMR AMONG FUNCTIONAL AND
PHYLOGENETIC COMPONENTS
Analyses showed that phylogeny explains a significant
fraction of the variation of mass-specific RMR, and
that the cost of growth is much higher than the cost
of maintenance. Only the selected independent functional and phylogenetic variables were used in the
partitioning analyses (for a description of the procedure of variable selection, see ‘Statistical Methods’).
The results of the first analysis (Fig. 3A) show that the
functional factors (log10 Body Mass and log10 BoneGR)
explain a significant part of the variation of log 10 massspecific RMR (93.98%, P = 0.001, fractions ‘a + b’ in
Fig. 3A) and that the phylogeny also explains a significant part of this variation (75.53%, P = 0.012, fractions ‘b + c’ in Fig. 3A). There is also an important
overlap between the percentage of variation explained
by these two sets of independent variables (73.32%;
69
fraction ‘b’ in Fig. 3A), that, unfortunately, cannot be
tested for statistical significance (Desdevises et al.,
2003). Finally, 3.81% of the variation of log 10 massspecific RMR (fraction ‘d’ in Fig. 3A) is explained
neither by phylogeny, nor by the functional factors
considered here. Two additional variation partitioning
analyses were carried out to gain insights into the
quantification of the cost of growth and the cost of
maintenance in growing amniotes (Fig. 3B, C). These
analyses show that the cost of growth is much higher
than the cost of maintenance in our sample of growing
amniotes.
TESTING THE HYPOTHESIS OF THE INDEPENDENCE OF
ENERGY ALLOCATION
Analyses showed that maintenance metabolism and
growth rates are not significantly related. On the
one hand, values of maintenance metabolism were
computed as residuals of log 10 raw RMR (an indicator of the global metabolism) to log 10 MassGR (an
indicator of the cost of growth). These residuals were
calculated by using a phylogenetically independent
slope (b = 0.708) obtained from an independent contrasts analysis (R2 = 0.650; P = 0.0009). On the other
hand, size-independent growth rates were computed
as residuals of log10 BoneGR (an indicator of growth)
to body mass (an indicator of size). These residuals
were also calculated by using a slope (b = 0.516)
obtained from an independent contrasts analysis
(R2 = 0.429; P = 0.0151). We used Model I regression
(least-squares) because ‘It is [. . .] the only technique
that produces residuals (observed Y − predicted Y)
that are exactly uncorrelated with X’ (Harvey &
Pagel, 1991: 180). Finally, we were able to test the
relationship between maintenance metabolism
(growth independent residuals of whole metabolism)
and growth rates (size-independent residuals of
growth) by using independent contrasts. This relationship was not significant (P = 0.226).
ALLOMETRIC
STUDY
Independent contrasts analyses show that log 10 raw
RMR (mL O2 h−1) scales to log10 Body Mass with a
slope of 0.933 (R2 = 0.637; P = 0.0011; Fig. 4A).
Figure 4B shows the distribution of raw data. On
the other hand, the relationship between log 10 massspecific RMR (mL O2 h−1 g−1) and log10 Body Mass is
not significant (P = 0.771) in our sample of growing
amniotes.
ESTIMATING
METABOLIC RATES IN EXTINCT TAXA
We computed the slope of the relationship between
geometry-corrected RMR and bone growth rate by
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70
L. MONTES ET AL.
Figure 3. Components of the variation of log 10 mass-specific resting metabolic rate (mL O2 h−1 g−1) in the analysed sample
of growing amniotes obtained by using a variation partitioning analysis (Desdevises et al., 2003). These fractions are:
functional (portions ‘a + b’), phylogenetic (portions ‘b + c’), phylogenetically structured functional variation (portion ‘b’) and
unexplained (portion ‘d’). Fraction ‘a’ is exclusively explained by the functional factors included in the analyses (cost of
growth and cost of maintenance). Finally, fraction ‘c’ is exclusively explained by the phylogeny. The functional variables
considered were both log10 Body Mass (g) and log10 BoneGR (µm day−1) (A), only log10 BoneGR (B) and only log10 Body Mass
(C).
using an independent contrasts analysis. The obtained
equation was: log10 geometry-corrected RMR =
0.799 × log10 BoneGR; R2 = 0.703; P = 0.0003 (Fig. 5A).
The y-intercept of this line (a = −0.584, Fig. 5B) was
calculated by using this slope (0.799) and the estimated x (0.368) and y (−0.289) values for the root of
the tree, as suggested by Garland et al. (1993) and previously done by Weathers & Siegel (1995). Figure 5B
shows the distribution of raw data around this predictive equation. The (geometry-corrected) quantification
of RMR used here is an indicator of the metabolic rate
by mass unit, excluding the cost of maintenance
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
RESTING METABOLIC RATE IN GROWING AMNIOTES
Figure 4. A, linear regression between contrasts of log 10
raw resting metabolic rate (RMR) (mL O2 h−1) and contrasts of log10 Body Mass (g). This relationship is highly
significant (P = 0.0011). Abbreviations correspond to the
splits between taxa (Fig. 2): Amn, Amniota; Arch, Archosauria; Che, Chelonia; Diap, Diapsida; Lac, Lacertidae;
Lep, Lepidosauria; Mamm, Mammalia; Pleu, Pleurodira;
Rod, Rodentia; Saur, Sauropsida; Var, Varanidae. B, scatter
plot of values of log10 raw RMR (mL O2 h−1) and log10 Body
Mass (g). Ap, Anas platyrhynchos; Cn, Crocodylus niloticus;
Cp, Cavia porcellus; Gg, Gallus gallus; Lv, Lacerta vivipara; Mm, Microcebus murinus; Mmu, Mus musculus; Mr,
Macrochelodina rugosa; Pm, Podarcis muralis; Ps, Pelodiscus sinensis; Ts, Trachemys scripta; Ve, Varanus exanthematicus; Vn, Varanus niloticus.
linked to the decrease of the surface to volume ratio
with increasing size.
DISCUSSION
Biologists gain insight into the understanding of
biodiversity by using two main general approaches:
experimental analyses of animal models and the
phylogenetic comparative method (Harvey & Pagel,
1991). These approaches are complementary: whereas
we can elucidate causal relationships by experimentation, the historical dimension is only available using
the comparative approach. Previous studies have
71
Figure 5. A, linear regression between contrasts of
log10 geometry-corrected resting metabolic rate (RMR)
(mL O2 h−1 g−0.67) and contrasts of log10 BoneGR (µm day−1).
This relationship is highly significant (P = 0.0003). Abbreviations correspond to the splits between taxa (Fig. 2):
Amn, Amniota; Arch, Archosauria; Che, Chelonia; Diap,
Diapsida; Lac, Lacertidae; Lep, Lepidosauria; Mamm,
Mammalia; Pleu, Pleurodira; Rod, Rodentia; Saur, Sauropsida; Var, Varanidae. B, scatter plot of values of log10
geometry-corrected RMR (mL O2 h−1 g−0.67) and log10
BoneGR (µm day−1). The slope and the y-intercept of the
line were calculated by using the above independent contrasts analysis (see text). Ap, Anas platyrhynchos; Cn, Crocodylus niloticus; Cp, Cavia porcellus; Gg, Gallus gallus;
Lv, Lacerta vivipara; Mm, Microcebus murinus; Mmu, Mus
musculus; Mr, Macrochelodina rugosa; Pm, Podarcis muralis; Ps, Pelodiscus sinensis; Ts, Trachemys scripta; Ve, Varanus exanthematicus; Vn, Varanus niloticus.
analysed hypotheses explaining the variation of metabolic rates either in animal models (Jørgensen, 1988;
in Bufo bufo; Steyermark, 2002; in Chelydra serpentina) or separately in ectothermic (Bennett & Dawson,
1976) and endothermic species (Nagy, 1987; Weathers
& Siegel, 1995; White & Seymour, 2005). In the
present study, we have tested some of these hypotheses in a sample of growing amniotes including both
ectothermic and endothermic species, using the phylogenetic comparative method.
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
72
L. MONTES ET AL.
PARTITIONING THE VARIATION OF MASS-SPECIFIC
RMR AMONG FUNCTIONAL AND
PHYLOGENETIC COMPONENTS
The first partitioning analysis shows that phylogeny
explains a significant portion of the variation of log 10
mass-specific RMR in our sample of growing amniotes
(75.5%; P = 0.012; fraction ‘b + c’ in Fig. 3A). Therefore, the inclusion of the phylogeny in the analyses is
completely justified. The set of functional variables
(cost of growth and cost of maintenance) also explains
a significant part of the variation of log 10 mass-specific
RMR (94.0%; P = 0.001; fractions ‘a + b’ in Fig. 3A),
which overlaps to a great extent with the portion
explained by phylogeny (fraction ‘b’ in Fig. 3A).
This last fraction has been called ‘phylogenetically
structured environmental variation’ (Desdevises
et al., 2003), and may correspond to synapomorphies
that have functional significance (Cubo, 2004). Finally,
the unexplained portion of the variation of massspecific RMR (fraction ‘d’ in Fig. 3A) may correspond to
some level of activity (stress, residual thermoregulation, residual digestion), as well as to some nonbiological causes such as measurement error or the use of a
linear model. Partitioning analyses were carried out
on the variation of mass-specific RMR because this
variable is an indicator of the metabolic rate by mass
unit (the use of geometry-corrected RMR may have led
to underestimating the cost of maintenance).
We tried to gain insight into the relative costs of
maintenance and growth by performing two complementary partitioning analyses. Our results suggest
that the cost of growth is much higher than the cost
of maintenance in our sample of young amniotes
(Fig. 3B, C). We expect that the portions of variation of
log10 mass-specific RMR explained by these functional
factors (growth and maintenance) may overlap to
some extent: in any given tissue, although the activities of some cell types (e.g. osteocytes in bone tissue)
are only linked to maintenance, the activities of other
cell types (e.g. osteoblasts in bone tissue) are involved
in growth and also in maintenance. However, this
overlap among functional factors cannot be assessed
with currently available methods. The development of
a variation partitioning method with three factors
(maintenance, growth, and phylogeny) may allow a
precise quantification of the fractions of the variation
of log10 mass-specific RMR exclusively explained by
growth, exclusively explained by maintenance, and
explained by the overlap of these two factors. We look
forward to this development.
Log10 BoneGR and log10 MassGR (two indicators of
the cost of growth) were considered as independent
variables in the preceding partitioning analyses
(log10 mass-specific RMR was the dependent variable).
This assumed that a sustained high bone growth rate,
or body mass growth rate, may involve high protein
turnover, which may require high oxygen consumption
and then produce high mass-specific RMR (Nagy,
2000). Indeed, the rate of growth may be involved in a
positive feedback because growth would contribute to
thermogenesis (‘[the] use of ATP to fuel growth is
manifested as increased heat production’; Peterson,
Walton & Bennett, 1999), being at the same time
strongly influenced by internal temperature. Accordingly, it seems realistic to consider growth rates
(BoneGR and MassGR) as independent variables
explaining the variation of log 10 mass-specific RMR.
TESTING THE HYPOTHESIS OF THE INDEPENDENCE OF
ENERGY ALLOCATION
As noted earlier, three competing hypotheses have
been proposed to explain the relationship between the
rates of growth and maintenance metabolism (Steyermark, 2002). Konarzewski (1995) analysed three periods of avian postembryonic development and found
evidence for the principle of allocation (according to
which there would be a trade-off between maintenance
metabolism and growth rate) for two periods and evidence for the hypothesis of independence of energy
allocation (according to which the amounts of energy
allocated to growth and maintenance are independent
from each other) for the third period of postembryonic
development. Our results show that maintenance
metabolism (growth independent residuals of raw
RMR) is not correlated with size-independent residuals of growth, thus supporting the hypothesis of independence of allocation. However, all these results
should be analysed with caution because both Konarzewski (1995) and the present study used calculated,
rather than directly measured, values of maintenance
metabolism. The problem is that it is difficult, perhaps
impossible, to measure the pure cost of maintenance of
growing amniotes by experimentally stopping their
growth processes. Steyermark (2002) attempted to
do so in an ectothermic species (C. serpentina) and
assumed that, because his experimental animals were
not fed for 3 days before and during measurements,
the calculated RMR would reflect only maintenance
metabolism. However, we are sceptical that growth
would be completely stopped, even in an ectothermic
species with indeterminate growth pattern.
ALLOMETRIC
STUDY
The allometric exponent of raw metabolic rate of adult
birds is 0.75 (Lasiewski & Dawson, 1967) and differs
from the scaling of metabolism in hatchlings, which is
proportional to body mass raised to the power of 0.86
(Klaassen & Drent, 1991). Weathers & Siegel (1995)
found a similar exponent (0.85) for the scaling of log 10
raw RMR in a sample of 27 species of birds, including
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
RESTING METABOLIC RATE IN GROWING AMNIOTES
for each species a value at hatching, a value midway
through growth, and a value near maturity. The slope
obtained here for the scaling of log 10 raw RMR
(mL O2 h−1) in our sample of growing amniotes
(b = 0.93, Fig. 4A) is also higher than the slopes that
correspond to the scaling of metabolic rate obtained
for subsamples of adult amniotes (b = 0.76 for placental mammals; b = 0.73 for birds; b = 0.80 for lizards,
and b = 0.86 for chelonians; Withers, 1992). According
to Wieser (1994), ‘Usually it is assumed that the scaling exponent of growth metabolism is close to 1.0, that
of maintenance metabolism around 0.75. Thus (. . .)
the value of b will approach 1.0 when metabolic cost of
growth predominate and 0.75 when costs of maintenance predominate’. The slope obtained here (b = 0.93,
Fig. 4A) is closer to 1.0 than to 0.75. This result may be
explained by the fact that, as showed above, the cost of
growth is much higher than the cost of maintenance in
our sample of growing amniotes (Fig. 3B, C).
On the other hand, because all these slopes of raw
RMR to body mass are smaller than unity, massspecific RMR (mL O2 h−1 g−1) is inversely correlated to
body mass in adult amniotes: b = −0.25 in mammals;
b = −0.28 in passerine birds; b = −0.31 in alligators,
and b = −0.18 in varanid lizards (data reviewed by
Wood et al., 1978). Our results show, however, that
log10 mass-specific RMR is not correlated with log 10
Body Mass in our sample of growing amniotes, probably because the scaling exponent of raw RMR
(b = 0.93, Fig. 4A) is close to 1.
ESTIMATING
METABOLIC RATES IN EXTICT TAXA
There is an abundant literature dealing with the problem of estimating metabolic rates of extinct taxa,
mainly archosaurs, using differences in bone tissue
types (de Ricqlès et al., 2001; Schweitzer & Marshall,
2001). However, the relationship between bone histodiversity and metabolic rate has never been tested in
extant species. In the present study, we provide, as a
first step, a positive linear relationship between log 10
BoneGR and log10 geometry-corrected RMR in growing amniotes (Fig. 5). We cannot directly measure
BoneGR in extinct species, but we can estimate it by
using Amprino’s rule (Castanet et al., 1996, 2000; de
Margerie et al., 2002, 2004), as done by Curry (1999),
Padian, de Ricqlès & Horner (2001), and Erickson
(2005) who estimated BoneGR from bone tissue types
in extinct archosaurs. In conclusion, it appears to be
feasible to estimate RMR from bone tissue in extinct
amniotes. These estimates would be especially interesting for Mesozoic non-avian theropod dinosaurs
(some of which bore feathers, presumably for thermoregulation; Benton, 1998) to investigate the origin
of avian endothermy. In our analysis, contrast
‘ARCHOSAURIA’ is placed far above the regression
73
line (Fig. 5A), indicating that the C. niloticus–Aves
split reflects large evolutionary change. Two hypotheses may explain this pattern, First, if we only consider
extant species, the principle of parsimony suggests
that C. niloticus retained the plesiomorphic condition
(ectothermy and low RMR) and Aves acquired the
derived condition (endothermy and high RMR). Second, de Ricqlès (1978) and Schweitzer & Marshall
(2001) have proposed an alternative hypothesis:
Triassic archosaurs may have been terrestrial and
may have had higher activity levels than modern crocodiles (which are amphibious in habit and adapted to
low energy life-styles). According to this last hypothesis, the last common ancestor of crocodiles and birds
may have had a high metabolic rate. An estimation of
metabolic rates of basal archosaurs using the predictive equation obtained in the present study may allow
these two competing hypotheses to be tested.
Palaeobiological estimates would also be interesting
in Permian and Triassic therapsids (some of which
already showed fibro-lamellar bone tissue suggesting
rapid growth; de Ricqlès, 1969; Chinsamy & Hurum,
2006) to investigate the origin of mammalian
entothermy. Surprisingly, the contrast that corresponds to the Mammalia–Sauropsida split (contrast
‘AMNIOTA’; Fig. 5A), although it implies the appearance of endothermy in the mammalian clade, is close
to the regression line. This may be linked to the fact
that, in our sample, two (of the three) species of mammals studied are altricial and not fully endothermic at
perinatal stages. These animals may have lower RMR
than growing precocial mammals of similar size. The
graphic representation of raw data around the line
obtained in the preceding independent contrasts analysis (Fig. 5B) shows that: (1) ectothermic amniotes
have low geometry-independent RMR (i.e. a low cost of
maintenance because they do not spend energy in heat
production and a low cost of growth because they grow
slowly) and (2) endothermic amniotes have high geometry-independent RMR (i.e. a high cost of maintenance
because they spend energy in heat production and a
high cost of growth because they grow quickly). However, in spite of these differences, and as noted in the
Introduction, maintenance and growth rates show
proportional representation in both ectothermic and
endothermic species (Wieser, 1994), thus justifying
the integrative analysis of all amniotes (ectothermic
and endothermic) together. This integrative analysis
shows that log10 geometry-independent RMR and log 10
BoneGR show a pattern of positive covariation (Fig. 5)
that can potentially be used in palaeobiological inference. However, as it frequently occurs in biology, there
are potential exceptions to this rule. In birds, altricial
hatchlings show lower RMR (Visser, 1998: 136) and
higher bone growth rates (Kirkwood et al., 1989) than
precocial ones of similar body mass. For a given total
© 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 92, 63–76
74
L. MONTES ET AL.
energy budget, altricial birds grow faster than precocial ones because heat supply is met through
parenting, the cost of thermoregulation is lower and,
consequently, more total energy can be shunted to
growth. Unfortunately, our sample does not contain
altricial birds. Future research may elucidate whether
the ratio of somatic production to total expenditures
is higher in growing altricial birds than in other
amniotes; in other words, whether altricial birds are
exceptions to the general rule of a proportional representation between maintenance and growth rates
(Wieser, 1994).
ACKNOWLEDGEMENTS
We thank the P. & M. Curie University (Paris) and the
UMR CNRS 7179 for the facilities provided to L. Montes and N. Le Roy during, respectively, their Master
M2 and Master M1 stages, both supervised by J. Cubo.
We thank R. Lafont, M. Laurin, S. Meylan, K. Padian,
and A. de Ricqlès for critical readings of preliminary
versions of this manuscript. We wish to thank M.
Girondot, J. Madiot, K. Daouès, and L. Fougeirol for
providing us with the animals, S. Bazin, K. Akkari,
M. T. Brisset, and C. Lajarille for caring for them, and
M. M. Loth for technical assistance in processing the
histological samples. L. Zylberberg provided helpful
advice with fluorescent microscopy. This research was
partly financed with funds from UMR 7179 (Dir. M.
Perret) and IFR 101 (Dir. R. Barbault).
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