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First published online March 27, 2009
Journal of Experimental Biology 212, 1101-1105 (2009)
Published by The Company of Biologists 2009
doi: 10.1242/jeb.026815
Individually variable energy management during egg production is repeatable across breeding attempts
1 Department of Biological Sciences, Simon Fraser University, 8888 University
Drive, Burnaby, Canada, V5A 1S6
2 Aberdeen Centre for Energy Regulation and Obesity (ACERO), Institute of
Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24
2TZ, UK
* Author for correspondence (e-mail: tdwillia{at}sfu.ca)
Accepted 28 January 2009
 |
Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: daily energy expenditure, energy management, inter-individual variation, plasticity, egg production, Taeniopygia guttata
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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;
Speakman, 2008). High energy
demands might constrain current reproduction because the supply of energy
during single breeding attempts is limited or they might generate negative
effects on future reproduction (Stearns,
1992). Thus, if parental investment during reproduction is
energetically costly then individuals capable of sustaining higher levels of
energy expenditure should be better at reproducing, e.g. they may have larger
clutch or litter size or rear more offspring (e.g.
Meijer et al., 1989;
Rogowitz, 1998;
Hammond et al., 1994;
Tinbergen and Dietz, 1994;
Speakman and Krol, 2005).
Despite the intuitive logic of these predictions, evidence for consistent
systematic relationships between DEE and individual traits (age, sex, body
size), environmental factors (e.g. food availability, temperature) or
reproductive- or fitness-related traits (e.g. number of offspring, survival)
is far from compelling in birds (Williams
and Vézina, 2001) (but see
Sanz and Tinbergen, 1999;
Fyhn et al., 2001) and mammals
(Speakman et al., 2003;
Speakman, 2008) [except for
relationships with temperature (Speakman,
2000; Anderson and Jetz,
2005)]. One reason for this might be the marked, and largely
unexplained, inter-individual variation in energy expenditure seen within any
population (e.g. Speakman et al.,
1994; Berteaux et al.,
1996; McKechnie,
2008; Careau et al.,
2008).
Recently, we suggested that female birds might utilise complex,
individually variable energy management strategies to meet the metabolic
demands of egg production (Vézina
et al., 2006). At the individual level, energy investment in egg
production in female zebra finches Taeniopygia guttata [i.e.
increased resting metabolic rate, RMR
(Nilsson and Raberg, 2001;
Vézina and Williams,
2005)] generated a wide spectrum of effects on DEE, from
overcompensation (net decrease in DEE) to additive effects [net increase in
DEE (Vézina et al.,
2006)]. Although all individuals appeared to compensate for the
cost of producing eggs via behavioural adjustments [decreased
locomotor activity (Vézina et al.,
2006)] (see also Houston et
al., 1995; Williams and
Ternan, 1999), this was individually variable. Consequently, net
increases in DEE were associated with relatively high reproductive effort
(large increase in RMR) and individuals with low reproductive effort (small
increase in RMR) were much better at avoiding this cost, and in some cases
even overcompensated for the elevated RMR via these behavioural
adjustments (Vézina et al.,
2006). We proposed that this inter-individual variation might help
explain why so few studies of free-living birds have found support for
positive relationships between energy expenditure and putative correlated
ecological or reproductive variables
(Williams and Vézina,
2001), and why some studies report contradictory results in
different years (e.g. Stevenson and
Bryant, 2000). Similarly, this may explain the lack of systematic
intraspecific relationships between resting or basal metabolism and
reproductive performance reported in many studies in mammals
(Hayes et al., 1992;
Johnson et al., 2001;
Krol et al., 2003;
Speakman et al., 2004;
Johnston et al., 2007) and
birds (Williams and Vézina,
2001) (see also Blackmer et
al., 2005). Here we show that the marked inter-individual
variation in the adjustment (or `plasticity') in DEE associated with egg
production in female zebra finches is repeatable, i.e. energy management
strategies are not only highly variable but are also consistent within
individuals over multiple breeding attempts.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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).
All birds received a mixed-seed diet (Panicum and white millet, 50:50), water,
grit and cuttlefish bone (calcium) ad libitum, and egg-laying birds
received 6 g of egg food supplement (20.3% protein, 6.6% lipid) daily, which
was always completely consumed by the following day. In the present experiment
(hereafter termed trial 2) we formed pairs (single-sex and breeding; see
below) using a sub-sample of the individual females that we studied previously
[(Vézina et al., 2006)
hereafter termed trial 1] and measured locomotor activity, food intake and
reproductive effort during a second, repeated cycle of egg laying. Repeat
measurement of breeding birds in trial 2 occurred 7 months after trial 1. Six
males died between the two trials and so six females in trial 2 were paired
with different mates. However, this only affected one measured trait: food
intake (see Results). Birds were housed in cages (61 cmx46 cmx41
cm) provided with an external nest box (15 cmx14.5 cmx20 cm); for
single-sex pairs access to the nest box was blocked with cardboard. During the
breeding experiment, nest boxes were checked daily between 10:00 h and 12:00
h, and all new eggs were weighed (to 0.001 g) and numbered. A clutch was
considered complete after two consecutive days with no new eggs. All
experiments and animal husbandry were carried out under a Simon Fraser
University Animal Care Committee permit (692B-94), following the guidelines of
the Canadian Committee on Animal Care. Our investigation of DEE adjustments associated with egg production in zebra finches used a repeated measures approach to compare DEE values of 22 females measured as non-breeders in single-sex pairs and at the one-egg stage of laying (sample sizes, non-breeders N=22, one-egg stage N=22). All birds used in the experiment were first paired as single-sex, non-breeding female pairs. Food consumption and locomotor activity (see below) were measured on day 5, 6 and 7 of the single-sex period, and DEE was measured from day 6 to day 7 using the doubly labelled water (DLW) technique (see below). On day 8, all birds were rearranged into breeding pairs and were given access to the nest boxes. Locomotor activity was monitored starting the following day until clutch completion. Food intake data were recorded the first 2 days after pairing (pre-laying) and again during laying beginning the day prior to laying of the first egg and during the following 4 days. All females had their DEE measured at the one-egg stage (i.e. on the day they laid their first egg) with estimates including a complete ovulation and laying cycle (second egg).
We monitored locomotor activity by using a micro-switch system connected to
a cage perch as described previously
(Williams and Ternan, 1999;
Vézina et al., 2006).
Food intake was determined by giving the birds 25 g day–1 of
seeds in an open 946 ml ZiplocTM food container placed on the cage floor
and weighing the seeds remaining in the container after 24 h. Williams and
Ternan showed that, on average, females eat slightly more food (4.5%) than
males and that this sex effect is significant only on the 2 days preceding the
first egg laid [P=0.016 and P=0.052, respectively, in their
table 1
(Williams and Ternan, 1999)].
Food intake per pair is therefore a good indicator of female food intake in
our experimental context, and we report the pair values (g
pair–1 day–1) as representative of female
energy input, as the proportion of seeds eaten by both sexes remains virtually
unchanged throughout our experimental protocol (for details, see
Vézina et al.,
2006).
|
We measured DEE using the DLW technique
(Lifson and McClintock, 1966;
Speakman, 1998) as described
before (Vézina et al.,
2006). This method has been previously validated by comparison to
indirect calorimetry in a range of birds (e.g.
Bevan et al., 1995;
Visser and Shekkermann, 1999;
van Tright et al., 2002). On
day one, the animals were weighed (±0.01 g) and a known mass of DLW
(ca. 67.7% 18O, 32.2% 2H) was administered
(i.m., 0.4 g 100 g–1 body mass). Syringes were weighed before
and after administration (±0.0001 g, Sartorius balance) to calculate
the mass of DLW injected. Blood samples were taken after 1 h of isotope
equilibration to estimate initial isotope enrichment
(Krol and Speakman, 1999).
Blood samples were immediately heat sealed into 2x50 ml glass
capillaries, which were stored at 4°C. Samples were also collected from
unlabelled birds to evaluate the background isotope enrichments of
2H and 18O [method C
(Speakman and Racey, 1987)].
Animals were recaptured and bled 24 h post-dosing to estimate isotope
elimination rates. Capillaries that contained the blood samples were then
vacuum distilled (Nagy, 1983),
and water from the resulting distillate was used to produce CO2 and
H2 [methods described in Speakman et al. for CO2
(Speakman et al., 1990) and in
Speakman and Krol for H2
(Speakman and Krol, 2005)].
CO2 production was converted into energy utilisation using a
conversion factor of 24.03 J ml–1 CO2, derived
from the Weir equation (Weir,
1949) for a respiratory quotient of 0.85. The isotope ratios
18O:16O and 2H:1H were analysed
using gas source isotope ratio mass spectrometry (Optima, Micromass IRMS and
Isochrom mG, Manchester, UK). We ran three high enrichment standards each day
alongside the samples and corrected all the raw data to these standards.
Isotope enrichment was converted to values of DEE using a single pool model as
recommended for this size of animal
(Speakman, 1993). There are
several alternative approaches for the treatment of evaporative water loss in
the calculation (Visser and Schekkermann,
1999). We chose the assumption of a fixed evaporation of 25% of
the water flux [equation 7.17 in Speakman
(Speakman, 1997)] which has
been established to minimise error in a range of conditions
(Visser and Schekkerman, 1999;
van Tright et al., 2002).
Data were analysed using SAS software (version 9.1, 2002–2003; SAS
Institute, Cary, NC, USA). We measured multiple traits at multiple times but
had a relatively small sample size (N=22 females) so we did not have
sufficient power to analyse data in a single comprehensive, multivariate
analysis. We focused our analyses on the change in DEE (
DEE), and
variability in DEE between trials in egg-laying birds (i.e. between
pre-laying and the one-egg stage), as a comprehensive within-trial analysis
with a larger sample size has been reported previously
(Vézina et al., 2006).
We first compared differences in mean trait values between trial 1 and trial
2, i.e. a `time' effect, using repeated measures ANOVA, or ANCOVA with
relevant covariates (GLM procedure; see Results). We calculated repeatability
for each trait following Lessells and Boag
(Lessells and Boag, 1987),
using the intraclass correlation coefficient based on variance components
derived from a one-way ANOVA. We then analysed correlates of DEE in egg-laying
birds during trial 2 only [in order to confirm results previously reported for
trial 1 (Vézina et al.,
2006)]. Finally, we compared individual variation in DEE
between trials to between-trial differences in all measured traits using
correlation analysis.
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Variation in food intake and locomotor activity
Pre-laying food intake was significantly higher in trial 2 than in trial 1,
by 0.72 g (23%; F1,17=8.95, P<0.01). At the
one-egg stage there was a significant male effect and malextrial
interaction for food intake (both P<0.025). For females paired
with the same male there was no difference in food intake between trials
(F1,15=0.55, P>0.4). For females paired with
different males there was a marginally significant difference in food intake:
4.5 vs 5.7 g day–1 in trial 1 and trial 2,
respectively (F1,5=5.38, P=0.068). However, on
average for all pairs food intake was only 3.1% higher during trial 2 compared
with trial 1 (F1,21=0.64, P>0.4) and food
intake was repeatable between trials (Table
1).
In both trials there was a marked decrease in locomotor activity between
the pre-laying and one-egg stage: trial 1, 1385±203 vs
854±89 hops day–1 (F1,20=13.75,
P<0.01); trial 2, 1426±221 vs 640±67 hops
day–1 (F1,20=14.09, P<0.01).
Locomotor activity did not differ among trials during pre-laying
(F1,20=0.02, P>0.8), but activity was lower
during trial 2 in laying birds (F1,20=4.59,
P=0.05). For both single-sex pairs and breeding pairs activity during
the 24 h DEE measurement period was highly correlated (R
0.76,
P<0.001) with activity measured over the whole time period
(2–4 days).
Variation in DEE and DEE
Non-breeding DEE, measured in single-sex pairs, was significantly higher
(by 10.5%) in trial 2 compared with trial 1 (F1,21=12.86,
P<0.01). However, controlling for body mass, this difference was
not significant (F1,25.7=1.17, P>0.2).
Furthermore, there was no difference in either absolute
(F1,21=0.42, P>0.5) or mass-corrected DEE
(F1,22=0.37, P>0.5) between trials at the
one-egg stage.
In trial 2, one-egg DEE was positively correlated with food intake (R22=0.58, P<0.01), and individuals with high one-egg DEE tended to have larger eggs (R22=0.36, P=0.097) and show a larger increase in body mass between the non-breeding and one-egg stage (R22=0.37, P=0.087). However, one-egg DEE was independent of locomotor activity, body mass, change in body mass (from non-breeding to laying) and other measures of primary reproductive output (laying interval, clutch size; P>0.15 in all cases).
DEE in individual females associated with egg production (calculated
as one-egg DEE–non-breeding DEE, where a positive value indicates a net
increase in DEE during laying) was not significant for either trial (trial 1,
t22=1.90, P>0.05; or trial 2,
t22=0.56, P>0.5), i.e. on average DEE was not
different for non-breeding or one-egg birds
(Fig. 1). However, in both
trials there was marked individual variation in DEE between the
non-breeding and one-egg stages (Fig.
1B). In trial 1, DEE varied between –17.3 and +24.1
kJ day–1 (–27.0% to +65.9% relative to non-breeding
DEE) and in trial 2 DEE varied between –17.7 and +11.8 kJ
day–1 (–30.8% to +23.3%). Both one-egg DEE and
DEE were repeatable (Table
1; Fig. 1).
Finally, the difference in DEE between trials (where negative values
indicate greater `compensation', i.e. lower DEE or smaller increase in DEE
relative to non-breeding values) was independent of the difference in all
measured traits between trials (body mass, activity, food intake, reproductive
effort).
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|
DISCUSSION |
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|
TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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), but also the individually variable adjustment or
`plasticity' in energy expenditure associated with egg production is
consistent or repeatable. The present study also confirms the key results of
our previous study (Vézina et al.,
2006), namely that there is no change in mean DEE associated with
egg production in female zebra finches, comparing non-breeding birds with
birds at the one-egg stage of laying, but that this masks marked, systematic
inter-individual variation in the change in DEE which we suggest indicates
individually variable energy management `strategies'. Although it is clear
that there are significant energetic costs associated with egg production
(e.g. Ward, 1996;
Nilsson and Raberg, 2001;
Vézina et al., 2006)
females appear to use behavioural mechanisms to modulate these energetic
costs. However, our study suggests that individual females do this to varying
degrees such that some females `overcompensate' for these changes with a
decrease in DEE during egg production whereas other females incur additive
costs with a net increase in DEE during egg production.
So far we have been unable to resolve the cause of the marked
inter-individual variation in DEE or changes in DEE associated with egg
production, and this is likely to prove difficult given the potential for (a)
behavioural adjustments allowing reallocation of energy among different
activities (e.g. Williams and Ternan,
1999; Speakman et al.,
2001; Husak,
2006); (b) intrinsic physiological adjustments such as organ
remodelling (Vézina and Williams,
2003; Speakman,
2008) or reallocation of energy away from other physiological
systems (e.g. Roberts et al.,
2004; French et al.,
2007); and (c) effects of extrinsic factors such as ecological or
social context (Speakman et al.,
2003). In the present study the only trait that was strongly
correlated with DEE was food intake (see also
Vézina et al., 2006).
In our opinion this is probably an effect, rather than a cause, of higher
energy expenditure, i.e. birds have to increase dietary intake to meet the
higher DEE (although it is possible that higher processing costs associated
with increased food intake might increase basal metabolic rate (BMR)
(Nilsson, 2002) which might in
turn contribute to increased DEE (but see Williams and Vézina). We
previously reported (Vézina et al,
2006) that females with the highest DEE at the one-egg stage
produced larger clutches and suggested that these females might benefit in
terms of reproductive investment despite the `additive' nature of reproductive
energy costs. We could not confirm the relationship between DEE and clutch
size in the present study, although there was some evidence to support the
idea that individuals with the highest one-egg DEE obtain benefits in terms of
reproductive output: there was a trend for DEE to be associated with a larger
egg size and a larger change in body mass, perhaps reflecting a higher mass of
developing reproductive organs
(Vézina et al., 2006).
By definition, repeatability is typically calculated using repeat measurements
of individuals under similar conditions, as in this experiment, and if DEE is
mainly set by extrinsic factors such as food supply (which was constant and
ad libitum in our experiment) then this might overestimate
repeatability related to intrinsic factors
(Speakman, 2000;
Speakman et al., 2003).
Indeed, we found that several components of individual reproductive investment
were repeatable including body mass, egg size and clutch size. Although there
was minor variation in DEE between breeding attempts this variation
itself was not explained by any of the other measured traits (e.g. body mass,
egg size, clutch size) or by differences in these traits between the two
breeding attempts. Determining the extent to which the repeatability of
DEE is robust under varying breeding conditions would obviously be a
priority for future studies.
Our study adds to the growing evidence for the repeatability of different
measures of energy expenditure, including BMR
(Bech et al., 1999;
Labocha et al., 2004;
Rønning et al., 2005)
(but see Russell and Chappel,
2007), RMR (Fournier and
Thomas, 1999; Vézina
and Williams, 2005) and DEE
(Potti et al., 1999;
Nespolo and Franco, 2007) (but
see Berteaux et al., 1996).
However, we have also shown that it is important to be able to measure
`plasticity' or change in energy expenditure associated with transitions of
physiological state (e.g. non-breeding to breeding) based on multiple
measurements of the same individual (see
McKechnie, 2008). An
increasing number of ecological and evolutionary studies have highlighted the
importance of considering how selection might drive the evolution of
phenotypic plasticity per se not just absolute trait values (e.g.
Piglucci, 2005; Brommer et al.,
2008); our study shows that it will be important to extend this
consideration of plasticity to physiological, endocrinological and metabolic
traits (see also Williams,
2008; Careau et al.,
2008).
LIST OF ABBREVIATIONS
DEE
|
Footnotes |
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Present address: Department of Biology, Universite de Quebec a Rimouski,
300 Allee Ursulines, Rimouski, Quebec, Canada, G5L 3A1 
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