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First published online September 14, 2007
Journal of Experimental Biology 210, 3407-3414 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.005090
Is basal metabolic rate influenced by age in a long-lived seabird, the snow petrel?
1 Department of Biology, Norwegian University of Science and Technology
(NTNU), NO-7491 Trondheim, Norway
2 Centre d'Étude Biologiques de Chizé (CEBC), Centre National
de la Recherche Scientifique, 79360 Villiers en Bois, France
* Author for correspondence at present address: Norwegian Institute for Nature Research (NINA), Division of Arctic Ecology, NO-9296 Tromsø, Norway (e-mail: borge.moe{at}nina.no)
Accepted 26 July 2007
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We performed a cross-sectional study of energy metabolism in relation to age in a long-lived seabird, the snow petrel Pagodroma nivea. In an Antarctic population that has been subject to a long-term research program, including annual banding of chicks since 1963, we measured BMR of individuals aged between 8 and 39 years. We show that the BMR of the snow petrel does not decrease with increasing age. BMR seems to be sustained at a fixed level throughout the investigated age-span.
We review this result in light of the disposable soma theory of ageing, and we discuss whether species-specific relationships between age and basal metabolic rate can be related to differences in maximum lifespan.
Key words: ageing, basal metabolic rate, body condition, disposable soma theory, diurnal rhythm, long-lived seabirds, oxidative stress hypothesis, Pagodroma nivea, senescence
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;
Holmes and Austad, 1995). The
`rate of living' theory, which arose nearly a century ago
(Rubner, 1908;
Pearl, 1922), advocated that
the degree of ageing and life expectancy was determined by the rate of energy
metabolism. This idea was based on the observation that many species with low
mass-specific metabolic rates had a relatively long lifespan.
The `free radical' theory (Harman,
1956), now often referred to as the `oxidative stress' hypothesis,
provided a mechanistic explanation for the rate of living hypothesis. Free
radicals and oxidants, termed reactive oxygen species (ROS), are produced as
by-products of oxidative phosphorylation (oxidative ATP production) in the
mitochondria (Cadenas and Davies,
2000). ROS are unstable and highly reactive molecules, and they
cause damage to DNA, proteins and lipids. The potential for damage depends on
the ROS production and the counteracting effects of antioxidant defence
mechanisms, and this is known as oxidative stress (e.g.
Beckman and Ames, 1998). The
consequent damage to cells and associated organs undergoing oxidative stress
is believed to be cumulative and thus to underlie the process of ageing.
Inter-specific comparisons made across and within classes show strong
relationships between body size and energy metabolism, as well as between body
size and lifespan. However, such comparisons also reveal large variation in
lifespan between animals with similar metabolic rates
(Speakman et al., 2002;
Speakman et al., 2003;
Speakman, 2005). This strongly
suggests that the relationship between energy metabolism and ageing differs
among different groups of animals.
Birds deserve special attention because they have a longer maximum lifespan
and fewer signs of ageing than expected from their body size and rates of
energy metabolism (Holmes and Ottinger,
2003). Long-lived seabirds especially have low rates of extrinsic
mortality (e.g. predation, contagious disease, starvation, weather-related
stress). Evolutionary theory predicts that species with low rates of extrinsic
mortality should benefit from evolving mechanisms that prevent age-related
cellular damage. On the other hand, species with high rates of extrinsic
mortality should not benefit from evolving special adaptations for combating
age-related cellular damage, because they usually die for other reasons than
senescence. Species with different rates of extrinsic mortality, and different
lifespans, are expected to have different optimal investment in somatic
maintenance and repair. This view is known as the `disposable soma theory of
ageing' (Kirkwood and Rose,
1991; Kirkwood and Austad,
2000). Mechanisms that prevent age-related cellular damage are
expected to have a genetic basis
(Kirkwood, 2002), and ageing
is thought to result from accumulation of cellular damage as a direct
consequence of evolved limitations in the genetic settings of maintenance and
repair functions.
Energy metabolism is a potential predictor for lifespan, but physiological
mechanisms underlying energy metabolism may also be subjected to age-related
effects themselves. It is a well-known phenomenon in humans that the basal
metabolic rate (BMR) declines with age
(Benedek et al., 1995;
Ryan et al., 1996;
Piers et al., 1998;
Hunter et al., 2001). For
simplicity, we do not distinguish between BMR and resting metabolic rate when
referring to published studies, because the functional difference between the
two is probably not relevant when addressing questions about age-related
effects. Decline in BMR with age has also been reported in rats
(Greenberg, 1999;
Even et al., 2001;
Miyasaka et al., 2003) and
dogs (Speakman et al., 2003).
However, age-related decline in metabolic rate does not seem to be universal
(O'Connor et al., 2002;
Promislow and Haselkorn, 2002;
Sukhotin et al., 2002;
Chappell et al., 2003).
In the present study we used the snow petrel Pagodroma nivea
Forster as a model species for examining age effects on physiological
performance. Snow petrels, like other high-latitude seabirds, exhibit a rather
active lifestyle with relatively high metabolic rates compared to other
non-passerine bird species (Weathers et
al., 2000; Hodum and Weathers,
2003). P. nivea is a medium-sized petrel, and, like other
procellariiform seabirds, is a long-lived species. The oldest individual in
our study population was ringed as adult in 1966 and is still alive. It must
be at least 46 years old, given that the minimum age at first breeding is 6
years. Mean life expectancy of snow petrels is 
30 years, given that the
annual adult survival is 0.95 and mean age at first breeding is 10 years
(Jenouvrier et al., 2005) (C.
Barbraud, personal communication). Thus, the maximum recorded lifespan of snow
petrels is about 3 times the predicted maximum lifespan for birds with the
same body mass (Lindstedt and Calder,
1976). Our study population has been subject to a long-term
ringing and monitoring program (see Materials and methods), which provided us
with access to individuals of known age. Hence, this population was very well
suited for our study: a cross-sectional study on the relationship between BMR
and age. In addition, the long-term study of this particular population
includes information about the average breeding success and the proportion of
breeders at the year of hatching
(Jenouvrier et al., 2005).
Breeding success and proportion of breeders vary substantially between years,
and this variation is partly explained by physical environmental variability
(Jenouvrier et al., 2005). We
also think that this variation reflects biotic environmental variability
(including food availability). We therefore used breeding success and
proportion of breeders at the year of hatching as proxies for overall
environmental conditions during early development (rearing conditions) of the
adults of known age.
Environmental conditions may affect early development and subsequent adult
phenotype (Gebhardt-Henrich and Richner,
1998). If early environmental conditions induce different adult
metabolic phenotypes, as was shown for a passerine species
(Verhulst et al., 2006), it is
important for two reasons to test for this. First, such an effect may
influence an entire cohort's BMR, which needs to be controlled for in any
statistical analysis of age-affected BMR. Secondly, data on early
developmental conditions and subsequent adult metabolic rate are scarce and
totally lacking for wild populations.
In the present study, we measured BMR, a key parameter of energy metabolism, as an indicator of physiological functioning. The aim of our study was to investigate whether physiological functioning was negatively affected by age in a long-lived seabird, the snow petrel, as could be expected if accumulated oxidative stress has caused cellular damage. On the other hand, if long-lived seabirds have evolved adaptations for preventing age-related oxidative damage up to old age, one would expect either that physiological functioning was not negatively affected by age, or there would be a delayed onset of such negative effects.
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; Barbraud and
Weimerskirch, 2001). Chicks have been ringed every year since
1963, and a substantial proportion of the current population consists of
individuals of known age, because ringed chicks have returned to the colony to
breed as adults later in life. Data on the average breeding success and the
proportion of breeders (i.e. number of breeding pairs relative to the number
of non-breeding pairs present in the colony) in the year of hatching
(Jenouvrier et al., 2005) were
used in this study as proxies for the overall environmental conditions in the
year of hatching (i.e. environmental conditions during early development) of
the adult snow petrels of known age. The present study was located outside the
sub-colonies used for demographic studies to avoid any disturbance to the
ongoing long-term demographic studies
(Jenouvrier et al., 2005).
Metabolic measurements
Rates of O2 consumption were measured in adult snow petrels
resting in the dark in thermoneutral conditions by open-flow respirometry
(Withers, 1977) using a
parallel two-chamber system. Outside air was dried using silica gel and pumped
through two 10-litre metabolic chambers with flow rates between 0.9 and
1.3 l min–1, depending on the size of the birds. The flow
rates entering each metabolic chamber were regulated with calibrated mass flow
controllers (Bronkhorst Hi-Tec, type F-201C-FAB-22-V, Rurlo, Holland), and the
flow rates were chosen to obtain O2 concentrations above 20% in the
chambers. Excurrent air was again dried, before a fraction of the air was
directed to the O2 analyser (Servomex type 244A, Crowborough, East
Sussex, UK). An automatic valve system located very close to the oxygen
analyser switched between excurrent air from the two chambers every 45 min.
Hence, the oxygen analyser alternated between measuring the O2
concentration from chamber 1 and chamber 2. The O2 analyser was
calibrated with dry atmospheric air (20.95%) and pure stock nitrogen. Any
changes in the O2 concentration readings in dry atmospheric air
post-experiment from those taken pre-experiment were controlled for by
assuming a linear drift. Measurements of the O2 concentration in
excurrent air were stored, along with the measurements of ambient temperatures
in the metabolic chambers, flow rates (l min–1) and valve
position (1 or 2) on a data logger (type Squirrel, Grant, Cambridge, UK), at
30 s intervals. The respirometry system was housed in a heated laboratory, and
we managed to maintain a relatively stable room temperature, which ensured
thermoneutral conditions inside the metabolic chambers. Ambient temperature at
the time of BMR measurement (range 13–19°C) was within the
thermoneutral zone of the snow petrels
(Weathers et al., 2000).
The metabolic measurements were performed on non-breeding individuals and
failed breeders only, mainly to avoid disturbance to breeding birds, but also
to avoid some confounding factors. These include temporal variation in BMR
that is known for breeding birds (Bech et
al., 2002) and the belief that non-breeders would carry reduced
food loads and thus more likely to be postabsorptive during the BMR
determinations. The measurements lasted for 10 h, to ensure that birds
could become rested and postabsorptive during this period. Consequently, we
assumed that heat increment of feeding was unlikely to significantly influence
our BMR measurements, although the petrels may metabolise small amounts of
stomach oils (Weathers et al.,
2000).
We performed measurements during two periods of the day. `Day' measurements
started (on average) at 11:30 h and ended at 20:50 h, while `night'
measurements started (on average) at 22:10 h and ended at 09:00 h.
High-latitude seabirds usually lack a diurnal rhythm in energy metabolism
(Bryant and Furness, 1995),
but snow petrels tend to be more active during the night than the day
(Bretagnolle, 1988).
Therefore, we tested for a potential diurnal rhythm in BMR in order to control
for it in the statistical analyses of BMR variation (see Statistics).
Oxygen consumption rates were calculated using formula 1d in Withers
(Withers, 1977), assuming a
constant RQ of 0.73, and corrected for wash-out delays in the system using the
method given by Niimi (Niimi,
1978). In this way, we obtained the instantaneous O2
consumption rates. The first 2 min of every 45 min interval were excluded from
the analyses to ensure that no air from the previous chamber remained in the
oxygen analyser at the time of measurement. Basal metabolic rate (BMR) was
defined as the lowest 20 min running average value during the 10 h sampling
period. The length of the interval (20 min running average) was chosen after
we had plotted the minimum values of the metabolic rate (MR), calculated in
five randomly selected experimental runs using intervals that varied from 5 to
60 min. This plot showed that the chosen interval was outside the part of the
curve where MR could be underestimated, i.e. above the part of the curve where
MR decreases rapidly with decreasing length of the intervals (see
Meerlo et al., 1997). Body
masses of the birds were weighed, to the nearest 1 g, before and immediately
after each experiment. A linear decrease in body mass during the experiment
was assumed when calculating the body mass at the time that BMR was
obtained.
Blood sampling, molecular sexing and hormone assay
The birds were bled from the alar vein with a 1 ml heparinised syringe
immediately after the metabolic measurement (i.e. they were bled in the
morning or in the evening). Blood samples were centrifuged and plasma and red
cells were separated and stored at –20°C until plasma was assayed.
The red blood cells were used for the molecular sexing. We used a molecular
method adapted from Fridolfsson and Ellegren
(Fridolfsson and Ellegren,
1999), which is described in further detail elsewhere
(Weimerskirch et al.,
2005).
Because snow petrels were held in the respirometer for a prolonged period
of time (10 h), we measured plasma levels of total corticosterone at the end
of the BMR measurement to see if birds were experiencing a stressful
situation. Plasma concentrations of corticosterone were determined by
radioimmunoassay at the CEBC as previously described
(Lormée et al., 2003).
All samples were run in one assay (intra-assay variation: 7.8%, N=5
duplicates). Blood samples that were collected within 3 min (sensu
`baseline level') (Lormée et al.,
2003; Romero and Reed,
2005) were considered to reflect the potential stress experienced
by the birds into the respirometer and not the stress of being removed and
handled outside the respirometer. Corticosterone levels (average: 6.0 ng
ml–1, N=7) were not significantly different to those
measured within 3 min in the field (average: 5.4 ng ml–1,
N=45; t-test, t=0.5, d.f.=50, P=0.6)
(Angelier et al., 2007a). The
majority of the BMR values were obtained during the last 1–3 h of the
metabolic measurement. These corticosterone levels, therefore, indicate that
we have obtained a measure of BMR with very little error due to stress.
Data handling and statistical analyses
A total of 67 snow petrels, either non-breeders or failed breeders from 50
different nests, were subjected to BMR measurements. BMR data of both pair
members from the 17 nests where both were measured were considered as
independent data, because BMR of the pair members were not significantly
correlated (BMR residuals: r=0.12, P=0.65; BMR:
r=0.39, P=0.12; where `BMR residuals' refers to residuals
from regression of BMR on body mass and `BMR' refers to whole animal BMR not
corrected for mass). The total sample of 67 snow petrels included 38
individuals of known age.
As male and female snow petrels differ greatly in body size
(Barbraud and Jouventin, 1998),
indices of body condition were calculated separately for each sex. Body
condition was calculated as the residual from a reduced major axis regression
of body mass against structural body size
(Green, 2001). To obtain one
variable for structural body size, a factor score (PC1) was extracted with a
principal component analysis including the length of the skull (head + bill),
culmen, tarsus, wing and the tip bill depth for males and females separately.
The PC1 for males explained 80% and the PC1 for females explained 82% of the
variation in these traits, and all traits correlated positively to the PC1
(r>0.85).
We used a general linear model (GLM) with type III sum of squares to perform analyses of covariance (ANCOVA) and variance (ANOVA). All variables were inspected graphically to ensure linearity, and correlation tests were used prior to analyses to ensure that there was no colinearity among the explanatory variables. All statistical tests were performed with SPSS version 14.0 (2005).
To test for any cyclic diurnal pattern in BMR, we transformed the time when
BMR was obtained from hours to radians (00:00 h=0
; 12.00 h=; 24:00
h=2), and included the term `cosine(time of BMR)' as a covariate in the
GLM analysis. To test whether BMR differed between the two measurement
periods, we included the term "Measurement (`Day' vs
`Night')" in the GLM analysis, where `Day' and `Night' refer to
measurements that were started during the day (at 11:30 h) and during the
night (at 22:10 h), respectively.
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The term cosine(time of BMR), of the general form cos(x), predicts
a function with the lowest values at (12:00 h) and the highest values at
0 and 2 (00:00 h and 24:00 h) (see
Fig. 2). We tested this
prediction with a non linear regression, and tested whether the estimate of
`B', in the term Cos(x+B), was significantly different from zero in
the following equation:
 | (1) |
).
No age-related effects on BMR or body condition
BMR was measured in 38 individuals of known age (range 8–39 years).
Age was not a significant predictor of the variation in BMR
(F1,34=1.1, P=0.3,
Table 2,
Fig. 3), and neither were two
proxies of environmental conditions at birth, i.e. average breeding success
and proportion of breeders at the year of hatching of the adult snow petrels
(F<0.8, P>0.38,
Table 2). Body mass, body
condition and the term cosine(time of BMR) were included in this analysis,
because they were found to be significant predictors of BMR in the analysis
from Table 1. However, the term
cosine(time of BMR) was not significant in this analysis, and it was therefore
not included in the final model that included age
(Table 2).
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Age did not significantly explain any of the variation in body condition (F1,36=2.2, P=0.15, Table 3). Furthermore, sex did not explain any significant amount of the variation in body condition (F1,35=0.2, P=0.65, Table 3), and neither did average breeding success and proportion of breeders at the year of hatching of the adult snow petrels (F<0.1, P>0.77, Table 3). BMR and body condition thus appear to be at rather fixed levels irrespective of age, at least in the investigated part of the lifespan of the adult snow petrels.
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) did not
find any significant decrease in BMR with age (ranging from 1–23 years)
in Leach's storm-petrels (Oceanodroma leucorhoa). In short-lived bird
species, we are only aware of two studies. BMR decreases with age both in
captive zebra finches (Taeniopygia guttata; B.M., B. Rønning
and C.B., unpublished data) and in natural populations of great tits
(Broggi et al., 2007). Those
studies are longitudinal and cross-sectional, respectively, and show rather
strong declines in BMR with age.
Our present study was cross-sectional (i.e. based on only one BMR
measurement per individual), and statistical analyses of cross-sectional data
are less powerful in detecting significant trends with age compared to
longitudinal data (i.e. repeated measurements within the same individuals over
a time span). One could argue that a larger sample size could have revealed a
significant trend and revealed a type II error in our analysis of BMR in
relation to age (Table 3,
Fig. 3). However, the
correlation coefficient and the slope were so close to zero that it is
unlikely that a larger sample size would have revealed a significant negative
relationship. Another problem with cross-sectional data is that we cannot
control for selective appearance or disappearance of individuals to/from the
population sampled (van de Pol and
Verhulst, 2006), which would occur if for example individuals with
low or high BMR had different survival rates.
A potential confounding factor in analysing the relationship between age
and BMR is a potential cohort effect in BMR due to different environmental
conditions during early development in different years. We used breeding
success and proportion of breeders at the year of hatching as proxies for
environmental conditions during early development, because we expected those
parameters to be good descriptors of the overall environmental conditions at
the year of hatching (Jenouvrier et al.,
2005). However, there was no significant relationship between BMR
and average breeding success or proportion of breeders at the year of
hatching, and consequently no indications that environmental conditions at
year of hatching induced differences in BMR among different cohorts.
Individuals reared under different early environmental conditions may attain
different adult metabolic phenotypes
(Verhulst et al., 2006), but
we would need data on food availability and food quality to further test these
relationships in the snow petrel.
We show that snow petrels have a cyclic diurnal rhythm in basal energy
metabolism. The energy consumption was highest at midnight (00:00 h), and this
is in accordance with a behavioural study showing that snow petrels are more
active during night (Bretagnolle,
1988). In inter-specific comparisons, the active-phase BMR is
reported to be 24% and 31% higher than the rest-phase BMR in non-passerines
and passerines, respectively (e.g. Aschoff
and Pohl, 1970). The novel aspect of our result is that we find a
significant diurnal rhythm in BMR of a high-latitude seabird and show that it
follows a cosine function. Energy metabolism of high-latitude seabirds,
breeding under continuous daylight, is not expected to differ, or to differ
much, between day and night (Gabrielsen et
al., 1988; Bryant and Furness,
1995). From the parameter estimates derived from
Table 1, we calculated that
predicted BMR was 16% higher at 00:00 h compared to at 12:00 h. One should,
however, treat this estimate carefully, because we would need more BMR values
around 00:00 h and 12:00 h to obtain a robust estimate of the difference in
energy metabolism between noon and midnight (see
Fig. 2).
The lack of any significant relationship between BMR and age (Fig. 3) was not confounded by any diurnal variation in BMR. The term cosine(time of BMR) was not significant in the analysis that included age and was not included in that final model (Table 2). Furthermore, the results were also the same if we used the residuals from the final model in Table 1, and analysed the relationship between age and residual BMR [BMR controlled for body mass, body condition and cosine(time of BMR)].
Senescence is a decline in physiological functioning and reproductive
performance with age. Our study detected no decline in physiological
functioning (BMR) with age (up to 39 years) in the snow petrel. This is
consistent with our data on reproductive success for this species
(Angelier et al., 2007a), which
shows that reproductive performance increases with age from 6 years to 12
years, then stabilises, and does not significantly decline in the oldest
petrels of the study. This supports analyses that suggest that birds in
natural populations maintain a high level of physical fitness into old age
(Ricklefs, 2000). We cannot,
however, rule out the possibility that an age-specific decline in
physiological functioning may occur in very old individuals, e.g. in
individuals older than 39 years. Senescence effects, measured as reduced
foraging performance and hormonal changes, have been documented in very old
grey-headed albatrosses (Thalassarche chrysostoma)
(Catry et al., 2006) and
black-browed albatrosses (Thallasarche melanophris)
(Angelier et al., 2007b),
respectively.
Our result indicating that snow petrels maintain a high level of physical
fitness into old age may support the disposable soma theory
(Kirkwood and Rose, 1991;
Kirkwood and Austad, 2000).
Long-lived seabirds have low extrinsic mortality rates
(Lack, 1968) and, from an
evolutionary consideration, should benefit from evolving mechanisms that
promote a high degree of somatic maintenance and repair which, consequently
prevent or delay age-related cellular damage. The pattern that two long-lived
bird species do not show age-related decline in BMR
(Blackmer et al., 2005)
(present study) and two short-lived birds species do show age-related decline
in BMR (Broggi et al., 2007)
(B.M., B. Rønning and C.B., unpublished) gives further support to the
disposable soma theory. There are two, not mutually exclusive, ways to apply
the disposable soma theory to our result. (1) BMR remains stable with age
because the somatic maintenance and repair mechanisms have prevented damage on
the macromolecules underlying the metabolic pathways in the mitochondria.
Hence, the structures in the mitochondria remain intact into old age and BMR
can function at the same levels. (2) The sustained BMR with age reflects the
energetic costs of having a high degree of somatic maintenance and repair into
old age in long-lived species. These two ways represent indirect and direct
relationships between somatic maintenance and repair and BMR, respectively. At
present, we do not know if it is possible to separate the two.
An example of a long-lived mammalian species with no age-related decline in
BMR is the naked mole rat (O'Connor et
al., 2002), which is the most long-lived rodent having a maximum
lifespan of more than 28 years
(Buffenstein, 2005). Other rat
species, with a shorter maximum lifespan compared to the naked mole rat, show
age-related decline in BMR (Greenberg,
1999; Even et al.,
2001; Miyasaka et al.,
2003). The decrease in BMR with increasing age in humans (e.g.
Piers et al., 1998) contrasts
the above mammalian and avian examples, because humans are also very
long-lived. We do not have a good explanation for this apparent paradox. It
might be that the onset of the decrease is delayed or that the rate of
decrease is slower in humans compared to more short-lived mammalian species,
but it is difficult to evaluate this from the available literature.
If the relationship between BMR and age is related to species-specific
maximum lifespan, one may expect the degree of decrease in BMR with increasing
age to be reflected in rates of senescence and in the degree of adaptations
for preventing age-related cellular damage. Such adaptations could be
associated with (1) better defences against ROS (oxidative protection), (2)
less production of ROS or/and (3) better repair of the damage caused by ROS.
Membrane properties of cells and mitochondria, such as fatty acyl composition
and mitochondrial uncoupling proteins, are suggested to be important both for
limiting oxidative damage and for affecting metabolic rate (e.g.
Speakman et al., 2004;
Criscoulo et al., 2005; Hulbert,
2006; Hulbert et al., in
press). At present, however, we can only speculate why different
species show different relationships between BMR and age.
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Acknowledgments |
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