Metabolic adjustments to increasing foraging costs of starlings in a closed economy
Zoological Laboratory, University of Groningen, PO Box 14, 9750 AA Haren, The Netherlands
* Author for correspondence at present address: Department of Evolution, Ecology and Organismal Biology, Ohio State University, 288 Aronoff Lab, 318 W 12th Avenue, Columbus, OH 43210, USA (e-mail: wiersma.6{at}osu.edu)
Accepted 23 August 2005
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Summary |
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Key words: flight cost, BMR, foraging reward rate, Sturnus vulgaris, body mass, pectoral muscle size
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Introduction |
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An approach that has frequently been used to study the physiological
consequences of food stress is complete or partial caloric restriction
(Daan et al., 1989;
Cherel et al., 1994
), which by
definition results in a decrease in DEE
(Fig. 1A). However, when
foraging success decreases in the real world, animals that are not
sit-and-wait predators have to spend more time and energy foraging for the
same amount of food. Everything else remaining equal, DEE is expected to be an
accelerating function of foraging costs per reward, because the extra energy
spent foraging must also be acquired, which again increases foraging time and
energy expenditure, and so on (Fig.
1B). Surprisingly, contrary to this simple prediction,
experimental tests found that DEE decreased with increasing foraging costs per
reward (Deerenberg et al.,
1998
; Bautista et al.,
1998
), thereby superficially resembling the results of caloric
restriction experiments. A possible explanation for this counter-intuitive
result was suggested by Fotheringham
(1998
): in his experiments,
starlings Sturnus vulgaris L. decreased food intake and body mass
with decreasing foraging reward rate when the number of flights needed for a
food pellet was fixed (e.g. 20 flights per reward). But when using variable
reward rates (rewarding each flight with a reward with e.g. probability 1/20)
they maintained food consumption and body mass (albeit that the range of
foraging reward rates was small). Since earlier studies of the relationship
between DEE and foraging costs per reward used fixed reward rates
(Deerenberg et al., 1998
;
Bautista et al., 1998
), this
aspect of the experimental design may explain why they found a decrease in DEE
with increasing foraging costs. Fotheringham speculated that cognitive
processes, such as motivation or memory, caused the differential response to
the variable and fixed reward rates. Whatever the mechanisms, since food
reward rates experienced by free-living animals will typically be variable,
applying variable foraging reward rates in laboratory studies may prove
essential when extrapolating the results to natural conditions.
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We studied captive starlings to test the prediction that birds increase DEE
with increasing foraging costs. We further quantified the (energy) dimensions
over which starlings adapt their physiology to the harshness of their
environment (mass changes, time and energy budgets, pectoral muscle size).
Following Fotheringham (1998)
we rewarded foraging effort with a certain probability that the birds will be
given a reward after a return flight, thereby creating a variable foraging
reward rate. We set the foraging costs per reward at three different levels,
referred to as a `rich', `intermediate' and `poor' foraging environment.
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Materials and methods |
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The L:D cycle was 14:00 h:10:00 h and food could only be obtained during the light period. For practical reasons the light period was from 00:00 h to 14:00 h, and night started and ended with a 10 min period of dim light. Fresh drinking water was always present, and water for bathing was presented 1 day per week. As a source of complementary nutrients, two mealworms were given three times each week, except during the periods of energy intake measurements. Ambient temperatures were 16.3±0.1°C (mean ± S.E.M.) during the night and 17.1±0°C during the day.
Food pellets (Trouvit Europe Eel, Trouw, France) consisted of proteins (44%), fat (30%) and fibre/ash (20.0%), complemented with vitamins and minerals (manufacturer's specifications). Average fresh mass of one pellet was 0.020 g, with water content 4%. Energy content was 24.7 kJ g-1 dry mass; one pellet therefore contained 474 J.
Experimental protocol
The experiment was performed with eight males caught in the wild and housed
in an outdoor aviary until the experiment. All birds had prior experience with
the system. At the start of the experiment all birds experienced a foraging
reward rate of 2.0 return flights per pellet (f/p; the `rich' environment).
After 1 week the foraging reward rate of four birds was gradually decreased to
6.3 f/p over a period of 3 weeks (the `poor' environment). The other four
birds remained in the rich environment. One bird kept losing mass when on a
rate of 5.6 f/p, and this bird was therefore kept on a foraging reward rate of
5.0 f/p. The birds stayed on these schedules for a further 2 weeks, and during
the last days of this period energy expenditure measurements were taken on all
eight birds. Next, the rich and poor feeding conditions were gradually changed
to the opposite state. In this transition period all birds remained on a
foraging reward rate of 4.0 f/p (`intermediate' environment) for 1 week to
measure their energy budget. Subsequently, foraging reward rates of the birds
formerly in the rich condition was steadily decreased over a period of 4
weeks, until one bird was on a rate of 6.3, one at 5.6 and one at 5.0 f/p. The
fourth bird died halfway through the experiment from an unidentified disease
and was left out from all analyses. The four birds that were initially in the
poor environment were gradually brought to a rich, 2.0 f/p regime.
Metabolic measurements
For basal metabolic rate (BMR) measurements the birds were taken from their
cages at the end of the light period and kept for the night in an open air
flow system for measuring rates of O2 consumption and
CO2 production. Starlings are post-absorptive after 60-75 min
(Karasov, 1990;
Levey and Karasov, 1994
).
During a measurement, a bird was sitting on a perch inside a dark 24 l
Plexiglas box at a temperature of 26.5°C, which is within the
thermoneutral zone of starlings (Kendeigh
et al., 1977
; Biebach,
1979
,
1984
). The air flow rate was
controlled by mass-flow controllers (5850S, Brooks, Rijswijk, The
Netherlands), that were calibrated with a bubble flow meter
(Levy, 1964
), set to deliver
40.0 l h-1. In- and out-flowing air was dried by passing through a
molecular sieve (3Å, Merck, Darmstadt, Germany). Gas analysis was done
using a paramagnetic O2-analyser (Servomex Xentra 4100,
Crowborough, UK) and CO2-analyser (Servomex 1440). The system was
calibrated before each measurement session using two 3-digit precision gas
mixtures of 20.0% O2/0% CO2 and 21.0% O2/1.0%
CO2 in N2. Measurements were recorded at 9 min
intervals. The rate of oxygen consumption was calculated from these
measurements and converted to the energy equivalent, while correcting for the
respiratory quotient, according to Brody
(1945
). BMR was taken to be the
minimum value of a 30 min running mean. Body mass was measured before and
after respirometry. Because the temperatures in the flight cages (on average
16.8°C) were within the thermoneutral zone
(Kendeigh et al., 1977
),
respirometer measurements could be applied for night-time energy expenditure
(Enight, kJ 10 h-1) estimates in the flight
cages without temperature corrections.
To estimate the metabolic rate of the starlings during the day when not flying, a separate series of trials was undertaken in which a different group of starlings was kept in smaller cages measuring 40 cmx80 cmx40 cm (heightxwidthxdepth), precluding flight activities. Eighteen birds had ad libitum food, and an additional six birds were restricted to 70% of the ad libitum food intake per day, a reduction similar to the 73% of food eaten by starlings in a poor environment compared to a rich environment. These individual trials lasted approx. 7 days, at the end of which their metabolic rate was monitored for 24 h by respirometry, while maintaining the light:dark schedule.
Daily energy expenditure
DEE was estimated from food consumption, faeces production and mass change.
Food consumption was measured by weighing the food in the pellet dispensers at
48 h intervals. All faeces were collected from the cages, and from the plastic
sheets covering the floors for this purpose. Faeces that remained on the
sheets after initial cleaning were removed with moist towels of known dry
mass. Faeces and towels were weighed after drying for 3 days at 70°C.
Energy content of dried food samples and of individual faeces samples were
measured using a bomb calorimeter (C5000, IKA, Heitersheim, Germany). DEE was
calculated from the metabolisable energy intake (MEI) and body mass changes,
according to the equation DEE=I-E-P, where
I is the gross energy intake, E the energy excreted and
P the energy cost of tissue accumulation or energy catabolised from
stored tissue, all in kJ day-1. P was estimated to equal
mass change x -18.0 kJ g-1 by accurately measuring energy
budgets of captive starlings that showed mass changes (S. Verhulst,
unpublished data; for method, see Masman,
1986). The assimilation efficiency was calculated as gross energy
intake minus energy content of the faeces divided by gross energy intake.
Body mass, pectoral muscle thickness and flight speed
Body mass was measured automatically to the nearest 0.1 g when birds were
on their feeding perch, and with an ordinary balance whenever a bird was
handled. In this study we used the average mass during the last hour of the
active period. Relative pectoral muscle thickness was measured using a `muscle
meter' developed at Max Planck Research Centre for Ornithology (Seewiesen,
Germany), which measured, to the nearest 0.1 mm, the distance from the breast
surface to a virtual plane perpendicular to the sternum crest, 3.0 mm sideways
of the sternum. Three measurements were taken at the location where the
sternum protruded furthest from the centre of the body, and the average value
was used.
Flight speed was measured early in the morning from video recordings taken from outside the cages through a one-way screen. The time from take-off to landing was measured with a stopwatch for a sample of flights.
Statistics
All mean values are given ± standard error of the mean
(S.E.M.). We analysed data with Generalised
Linear Models (GLM) and Generalised Linear Mixed Models (GLMM) using SPSS (v.
12.0, SPSS Inc.), except where otherwise stated. We controlled for individual
differences by including individual as fixed (GLM) or random (GLMM) effect in
the models.
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Results |
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Total nocturnal mass-specific MR decreased with decreasing food availability in the same way as whole-body MR (Fig. 2). In contrast, mass-specific BMR (BMRms) was independent of food availability (Table 1).
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In the poor environment the pectoral muscle was 1.1±0.2 mm thinner than in the rich environment (Table 1), but between the intermediate and poor environment there was no difference (paired t-test, t6=0.62, P=0.56).
Flight costs
Flight costs of starlings were previously estimated, in the same flight
cages, at 20.5±0.93 W (Hambly et
al., 2004) using labelled bicarbonate
(Speakman and Thomson, 1997
;
Hambly et al., 2002
). If
flight costs were independent of food availability, the energy allocated to
flight would have quadrupled (Eflight in
Table 2A), due to the fourfold
increase in flight time. However, flight costs were measured in the rich
environment only, and daily flight costs estimated using instantaneous flights
costs of 20.5 W did not fit the energy budget, in particular in the poor
environment (given the observed time spent flying and a flight cost of 20.5 W
the energy expenditure when not flying would have to be substantially lower
than BMR, which is impossible; see below for calculations). We therefore made
independent estimates of flight costs for all three experimental treatments
using the DEE, activity and respirometer measurements. We estimated how much
energy was spent on activities other than flight, and calculated flight costs
under the assumption that the difference between the non-fly budget and the
total energy expenditure was spent on flight.
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Nocturnal energy expenditure was measured directly
(Enight, Table
1). For non-flying birds during day-time we multiplied
MRnight with a scaling factor determined independently using 24 h
respirometer measurements on a different group of starlings. These birds
stayed at 20.8±0.2°C, were either on ad libitum food or
food-rationed, had drinking water available and experienced a L:D cycle of 12
h:12 h. We used respirometer boxes that were small enough to constrain
activity, and most of the time was spent sitting (P. Wiersma, unpublished
observations). From these measurements we calculated MRday and
MRnight. MRday was correlated with mass
(r=0.44, N=24, P=0.032), but more importantly,
MRday and MRnight were strongly correlated
(Fig. 4). We calculated the
ratio of MRday to MRnight (1.62±0.03), and
applied this figure to our current MRnight data to predict
MRday. Our estimate can be compared with Aschoff and Pohl's
allometric relationship for passerines
(Aschoff and Pohl, 1970), on
the basis that a ratio of 1.42 for RMR
/RMR
is predicted. The small difference with our value may be explained by the fact
that Aschoff and Pohl used birds permanently in the dark while our birds had
light during the day, which is likely to result in higher energy expenditure.
Using the estimated MRday in the budget resulted in estimated
flight costs of 17.5±0.9 W. Estimates for the rich and intermediate
environments did not differ from Hambly et al.'s measured value (one sample
t14<1.64, P>0.12;
Hambly et al., 2004
), but the
estimate from the poor environment was significantly lower
(t14=2.72, P=0.017). We therefore estimated
flight costs in the rich and intermediate environments at 20.5 W, and consider
17.5 W our best estimate for the poor environment.
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Discussion |
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Our results can be compared with two earlier experiments with starlings
foraging in a closed economy. Bautista et al.
(1998) used fixed reward rates
and found that DEE decreased from 144 kJ to 107 kJ when the environment
changed from rich to poor. Fotheringham
(1998
) used variable reward
rates (as did we) and found that DEE was approximately 169 kJ day-1
independent of reward rate [estimated by us from gross food intake of birds
with variable reward rates: approx. 19 g day-1, and energy content
and assimilation efficiency from Bautista et al.
(1998
), who used the same
food]. This is comparable to the DEE of our birds in the rich environment
(Table 1). The poor environment
in our experiment was much harsher than the poorest environment in
Fotheringham's experiments: birds in his experiments flew ±4 km in the
poorest environment, which is only 13% of the flight distance in the poor
environment in our experiment.
BMR
In the poor environment BMR was 19.6% lower than in the rich environment
(Table 1), resulting in an
energy saving of 14.6 kJ day-1, in qualitative agreement with
comparable studies (Deerenberg et al.,
1998; Bautista et al.,
1998
), and the observed effect of exercise (independent of
foraging) in zebra finches (Nudds and
Bryant, 2001
). The energy savings on BMR were probably for a large
part due to mass changes because BMRms did not differ between
foraging environments. This contrasts with the results of lower
BMRms in the poor environment in related studies
(Deerenberg et al., 1998
;
Bautista et al., 1998
). That
BMRms was constant is remarkable considering the strong effect of
food availability on body mass (-20% in the poor environment). Either body
composition did not undergo major changes (which seems unlikely given that the
birds became lighter, not smaller), or energy was saved in other ways, e.g. by
lowering body temperature (Tb), in which case the constant
BMRms may be coincidence. Hypothermic responses to cope with energy
shortage are common in birds (McKechnie
and Lovegrove, 2002
), which suggests that this may be a plausible
explanation. Savings during the entire night were larger than expected on the
basis of BMR alone because metabolic rate decreased more quickly during the
night when foraging in the poor environment
(Fig. 2). This could also be
due to an accelerated decrease of Tb in the course of the
night.
Although our finding that BMR was lower in the poor environment is
consistent with comparable studies, it contrasts with the notion that BMR is
adjusted to work load (Gelineo,
1964; Arieli et al.,
1979
; Daan et al.,
1989
; Speakman and McQueenie,
1996
; Williams and Tieleman,
2000
; Speakman and Selman,
2003
). According to this notion we would have predicted BMR to be
higher in the poor environment, because of the increase in daytime energy
expenditure this induced. Apparently, birds make different physiological
adjustments under different ecological circumstances, but it is not evident
what triggers these different responses. One could argue that metabolic rate
was somehow constrained in the poor environment, forcing the birds to reduce
their BMR. However, this is not consistent with the observation that a
decrease in BMR was already present in the intermediate environment, while DEE
increased further when birds were foraging in the poor environment. Possible
constraints on DEE will be discussed below. The experimental protocol followed
by our starlings can be looked upon as a training scheme for endurance
training, and we see similarities with studies on human exercise physiology.
Westerterp (2001
) points out
that `novice' trainees for the half-marathon lose body mass and concomitantly
lower night-time metabolism. Only professional athletes achieve an increase of
BMR at the same mass during training (hinting that a suite of changes are
involved). It seems our starlings `acclimate' to a training programme in much
the same way as `average' human beings do.
Flight costs
We previously estimated starling flight costs in our system at 20.5 W
(Hambly et al., 2004; rich
environment only). On the basis of this estimate, our birds would have spent
39.7 and 167.4 kJ day-1 on flying in the rich and the poor
environment respectively (Eflight in
Table 2A). However, these
estimates are too high to fit the energy budget in the poor environment. Given
flight costs of 20.5 W, and the nocturnal energy expenditure as measured, the
remaining energy in the poor environment for diurnal metabolic rate when not
flying would be 0.87xBMR, which is clearly impossible (see
Table 2A for calculations).
Given that total and nocturnal energy expenditure can be measured with
reasonable precision, this indicates that flight costs in the poor environment
must have been lower than 20.5 W. This would in itself not be surprising,
given the low mass in the poor environment, and the effect of mass on flight
costs (Pennycuick, 1975
;
Rayner, 1979
; but see
Kvist et al., 2001
). Note,
however, that we could not confirm this directly in our experiment, in the
sense that we found no treatment effect on our indirect estimates of flight
costs (Table 2A). However, on
the basis of an (interspecific) allometric equation of flight costs
(Nudds and Bryant, 2000
), one
would predict that flight costs should decline by 17% from the rich to the
poor environment. This is very close to our budget results (-15%, from 20.5 W
to 17.5 W), although we acknowledge that direct measurements of flight costs
in different foraging environments will have to be carried out to ascertain
lower flight costs in the poor environment.
Effects of time and treatment order
In the course of the experiment the birds lost 6.8±1.6 g body mass
(Fig. 3). At the same time
flight speed increased slightly by 0.12±0.04 m s-1
(Table 1), which may be due to
the lowering of body mass. Treatment had no effect on assimilation efficiency,
but there was a small increase over time: 0.037±0.012 from the first to
the third measurement (Table
1). Treatment order had no effect on any variable except
mass-specific BMR, with birds that started in the rich environment having a
0.99±0.36 mW higher BMRms. Since the effect was small and
statistically weak (P=0.04), suggesting it may be a spurious result,
we will not further discuss it.
Starving in the midst of plenty?
Compared with free-living birds, mass was exceptionally low when birds
foraged in the poor environment (Cramp and
Perrins, 1994), suggesting an energy shortage despite the
substantial increase in foraging effort and DEE. Energy shortage is also
suggested by the accelerated decline in metabolic rate in the course of the
night. Given that food availability was in principle unlimited, one could
argue that the birds were starving in the midst of plenty. We now examine some
hypotheses that could explain why birds did not increase their foraging effort
in the poor environment to the level required to maintain the same mass as in
the rich environment. To this end we first summarise our findings
(Fig. 6) to illustrate the
effects of the metabolic adjustments to foraging conditions on the total
energy budget. We extended this overview with estimates of the required energy
and time budget for the hypothetical case of birds in the poor environment
that maintain mass and activity specific metabolic rates as in the rich
environment. By lowering mass in the poor environment, and hence maintenance
metabolism and probably flight costs, the birds achieved a flight time
reduction of 34%, and a 37% reduction in DEE compared with the expected DEE in
the absence of such responses (these two percentages differ slightly due to
the effect of foraging environment on flight speed;
Table 1). Reduced flight costs
and maintenance metabolism resulted in approx. 15% reduction in DEE, but the
flight time reduction brings about the greatest energy savings, namely approx.
24%. The 27% energy saved during the night gives rise to a further 5% lower
DEE.
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Hypothesis 1: time constraints
The length of the working day (14 h) could be insufficient to collect the
food required to maintain mass. This seems unlikely however, because in the
poor environment birds still spent only 16.2% of the light period on flying.
This increases to 42% of daytime spent on foraging when food handling time is
taken into account. Using our energy expenditure measurements
(Table 1), we calculated that
the flight time should have increased to 25% to maintain mass in the poor
environment. Including turning time on the perch and food handling time this
would add up to 65% (Fig. 6),
which would still leave 35% `free time'. Since there were no other notable
time-consuming activities, we conclude that time was not a limiting factor.
Energy expenditure can be constrained by extrinsic or intrinsic constraints
(Tinbergen and Verhulst,
2000), but available foraging time seems the only extrinsic
constraint present in our system. Since this does not seem an adequate
explanation we now turn to intrinsic constraints.
Hypothesis 2: metabolic constraints
It has been suggested that sustained energy expenditure is constrained to
approximately 4xBMR (Drent and Daan,
1980), and birds spent 3.7xBMR in the poor environment,
close to this limit. Furthermore, the required DEE to maintain mass in the
poor environment (353 kJ; Fig.
6),combined with the BMR in the rich environment, would result in
4.7xBMR. Although this is high, it is still within the range of values
observed (Daan et al., 1990
).
More importantly, DEE in the poor environment was at the lower end of the
range of values reported for free-living starlings feeding nestlings
(Fig. 5), suggesting that some
increase in DEE was certainly possible. We did not notice symptoms indicating
exhaustion, such as difficulties in flying or lethargic behaviour, but cannot
rule out that such effects would have occurred when birds had increased their
foraging effort to the level required to maintain mass. In conclusion, there
was at least some scope for an increase in DEE from an energetic perspective,
suggesting that metabolic constraints do not explain why mass was not
maintained in the poor environment.
Hypothesis 3: digestive constraints
Digestive bottlenecks can play an important role in foraging decisions
(Kersten and Visser, 1996).
Starlings have food retention times of 53-59 min, independent of diet
(Levey and Karasov, 1994
), and
we therefore assumed that hourly intake rates over the light period are equal
to, or below, maximum sustainable rates. The average food consumption for the
different birds was low at 0.56-0.80 g h-1, well below the maximum
hourly consumption rates (range 0.98-1.4 g h-1). We therefore
conclude that DEE was not constrained by a digestive bottleneck.
Hypothesis 4: negative foraging benefits
When flight costs increase with mass, at some mass level the net energy
gain of foraging will become negative, and this could have prevented birds
from maintaining high mass in the poor environment. We therefore calculated
foraging efficiency and net intake rate in the poor environment for birds with
their actual mass and the mass maintained in the rich environment. The effect
of body mass (via flight and resting metabolism costs) on these
foraging currencies was rather small (Table
3). Both currencies became much less attractive when foraging
reward rate decreased, but lowering of flight costs through mass loss did not
result in substantial improvement. Certainly, no currency would drop to
negative values. There is therefore no indication that effects on foraging
currencies constrained mass changes.
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Hypothesis 5: cognitive processes
Fotheringham (1998) showed
that under variable rewards the birds maintained mass and food consumption,
but when reward rates were fixed the birds' mass and food consumption
decreased when foraging costs increased. We therefore used variable reward
rates in our experiments, but cannot exclude the possibility that further
modifications in the reward schedule would result in even higher work rates in
the poor environment.
Hypothesis 6: ultimate considerations
Given the absence of conspicuous constraints that prevent birds from
maintaining a higher mass and energy expenditure in the poor environment, the
response to deteriorating foraging conditions can be considered as an
optimality problem, i.e. in terms of fitness consequences of different
options. Although we cannot estimate the fitness benefits of maintaining high
mass, it seems reasonable to assume that if benefits of high mass vary they
are likely to be higher in the poor environment, because the probability that
food availability drops below a critical level due to stochastic variation is
higher when foraging conditions are poor. This suggests that failure to
maintain mass in the poor environment is related to fitness costs associated
with the high foraging effort this requires
(Fig. 6). DEE in the poor
environment was slightly lower than in brood-rearing starlings
(Fig. 5), but brood-rearing
birds accrue fitness benefits from a high DEE (i.e. offspring production),
which are absent in our experiments. Absence of such benefits may be part of
the explanation why birds were not motivated to further increase their DEE.
There is growing evidence that there are trade-offs between work rate and
different aspects of somatic maintenance and repair such as immune function
(e.g. Sheldon and Verhulst,
1996; Verhulst et al.,
2005
), and protection against oxidative damage
(Wiersma et al., 2004
;
Alonso-Alvarez et al., 2004
).
It is therefore plausible that such effects would entail a cost of increasing
work rate, but whether such effects really occur in our study system remains
to be demonstrated. Nevertheless, given that we consider all other hypotheses
less likely, and that trade-offs between work rate and somatic maintenance
have been demonstrated in other systems, we consider the costs of increasing
work rate the most likely explanation for our finding that birds do not
maintain mass in the poor environment.
In conclusion, when trying to understand the effect of food availability on animal behaviour it is important to be aware of the flexibility of the energy budget. For example, energy saving that may be achieved through physiological adjustments may have significant consequences for individual-based modelling exercises, which explore the relationship between food supply, individual behaviour and population dynamics. Surprisingly, to our best knowledge field data on the relationship between foraging costs per reward and DEE or BMR still have to be collected.
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Acknowledgments |
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