Metabolic rate varies among individuals and within single
individuals depending on their behaviour and physiological state.
Genetic variation is known to affect the metabolic rate of insects
(Laurie-Ahlberg et al., 1985; Nespolo et al., 2007), and quantitative
trait locus (QTL) mapping has revealed associations between
particular chromosomal regions and metabolic traits in Drosophila
melanogaster (Montooth et al., 2003). However, not many specific
loci linked with variation in metabolic rate have been identified so
far, although this situation is likely to change with the rapid
development of genomic tools even for non-model species (Ellegren
and Sheldon, 2008).
One example of a candidate gene associated with flight metabolic
rate is the polymorphic malate dehydrogenase (allozyme) locus in
honeybees (Coelho and Mitton, 1988; Harrison et al., 1996). Different
electromorphs of the enzyme have been shown to be associated with
different levels of metabolic rate. Another promising candidate gene
is phosphoglucose isomerase (Pgi), which codes for the glycolytic
enzyme PGI, well known for functional variation in Coliasbutterflies
(Watt, 1977; Watt et al., 2003) and in willow beetles (Dahlhoff et al.,
2008; Dahlhoff and Rank, 2000). PGI is known to be sensitive to
temperature. Different forms of the enzyme vary in their kinetics and
thermal stability: homozygous isoforms with high activity (Vmax/Km)
tend to be thermally unstable whereas those isoforms that tolerate
high temperature have low enzymatic activity (Watt, 1983).
Heterozygous forms of the enzyme may combine superior kinetics
with good thermal stability, affecting organismal performance and
fitness (Watt and Dean, 2000). In the Glanville fritillary butterfly
(Melitaea cinxia), flight metabolic rate varies between the PGI
allozyme electromorphs (Haag et al., 2005). Differences between PGI
genotypes have recently been traced to single nucleotide
polymorphisms (SNPs) in the Pgi sequence (Orsini et al., 2009), and
a single SNP in Pgi is sufficient to explain a significant amount of
variation in flight metabolic rate and dispersal in the Glanville fritillary
(Niitepõld et al., 2009).
Flight is essential for butterflies and many other insects. Flight
capacity may have direct fitness consequences because of processes
such as escape from predators, foraging and searching for oviposition
sites. The long-term persistence of the Glanville fritillary in
fragmented landscapes is dependent on frequent dispersal between
local populations and colonisation of new habitat patches (Hanski
and Ovaskainen, 2000). Flight capacity may also have indirect
consequences in the form of trade-offs with other energy-demanding
processes such as reproduction (Nespolo et al., 2008; Saglam et al.,
2008). The need for a high flight capacity may be reflected in the
cost of maintenance, namely in the resting metabolic rate, but while
a positive relationship between maximum and minimum metabolic
rates is well established for vertebrates (Bennett and Ruben, 1979;
Dutenhoffer and Swanson, 1996; Hinds et al., 1993; Walton, 1993;
White and Seymour, 2004), it is less clear what this relationship is
in invertebrates and in insects in particular (Niven and Scharlemann,
2005; Reinhold, 1999).
The goal of this study was to identify factors that affect resting
and flight metabolic rates in pupae and adults of the Glanville
fritillary butterfly. Apart from the effects of the time of the day
and body mass on metabolic rates, I also examined the effect of
the Pgi genotype and its interaction with temperature. The aim was
to investigate how molecular variation in the Pgi locus translates
to physiological performance under variable environmental
conditions. The individual-level correlations between mass-
independent pupal (MRpupa), resting (RMR) and peak flight
(MRpeak) metabolic rates were examined to assess whether the flight
metabolic rate could be predicted with measurements of the
minimum metabolic rate.
The Journal of Experimental Biology 213, 1042-1048
© 2010. Published by The Company of Biologists Ltd
Genotype by temperature interactions in the metabolic rate of the Glanville
Department of Biological and Environmental Sciences, University of Helsinki, FI-00014, Helsinki, Finland
Accepted 2 December 2009
Metabolic rate is a highly plastic trait. Here I examine factors that influence the metabolic rate of the Glanville fritillary butterfly
(Melitaea cinxia) in pupae and resting and flying adults. Body mass and temperature had consistent positive effects on metabolic
rate in pupae and resting adults but not in flying adults. There was also a consistent nonlinear effect of the time of the day, which
was strongest in pupae and weakest in flying adults. Flight metabolic rate was strongly affected by an interaction between the
phosphoglucose isomerase (Pgi) genotype and temperature. Over a broad range of measurement temperatures, heterozygous
individuals at a single nucleotide polymorphism (SNP) in Pgi had higher peak metabolic rate in flight, but at high temperatures
homozygous individuals performed better. The two genotypes did not differ in resting metabolic rate, suggesting that the
heterozygotes do not pay an additional energetic cost for their higher flight capacity. Mass-independent resting and flight
metabolic rates were at best weakly correlated at the individual level, and therefore, unlike in many vertebrates, resting metabolic
rate does not serve as a useful surrogate of the metabolic capacity of this butterfly.
Key words: allometry, flight, genotype by environment interaction (G?E), insect, metabolism, phosphoglucose isomerase.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
1043 Pgi effects on butterfly metabolic rate
MATERIALS AND METHODS
Study species and design
The Glanville fritillary butterfly (Melitaea cinxia L.) is a north-
temperate butterfly with a geographical distribution ranging from
west Europe to south Siberia (Tolman and Levington, 1997). The
species and its metapopulation dynamics have been intensively
studied in the Åland Islands in SW Finland since 1991 (Ehrlich and
Hanski, 2004; Hanski, 1999). These studies have addressed
questions such as the effect of habitat fragmentation on population
dynamics (Hanski and Ovaskainen, 2000) and inbreeding depression
in natural populations (Saccheri et al., 1998). Recent advances in
molecular techniques have made it possible to start building up a
genome-level understanding of ecological processes (Vera et al.,
2008). A major focus of the past work has been on dispersal, which
is known to be functionally linked with flight metabolic rate
(Niitepõld et al., 2009).
The present study consisted of two experiments. In the first
experiment, the resting (RMR) and peak flight metabolic rates
(MRpeak) of adult females were measured under suboptimal (26°C)
and optimal (32°C) temperatures. In the second experiment, females
were first measured as pupae and subsequently as adults at rest and
in flight. The latter measurements were taken over a range of
Experiment on resting and flight metabolic rates in
suboptimal and optimal temperatures
The experimental individuals originated from the Åland Islands and
were the offspring of a generation kept in a large outdoor cage
(Saastamoinen, 2007). The parents had been collected in the field
as newly eclosed adults. The larvae were reared in controlled
laboratory conditions (12h:12h L:D, and 25°C:20°C). Larvae had
constant access to leaves of the host plant Plantago lanceolata. After
eclosion butterflies were marked and weighed and a small piece
from the edge of the hind wings (max. 2mm?2mm) was cut for
The metabolic rates of 71 females were measured as CO2
emission rate using flow-through respirometry on the second full
day after eclosion. Each individual was measured once, either in
suboptimal (26°C) or optimal temperature (32°C). RMR and MRpeak
were measured during the same trial, first RMR, when the
respirometry jar was covered with a black cloth, then the
measurement of MRpeak, when the cover was removed and the
individual was exposed to UV light and agitated to fly. Individuals
were kept in a 1-l cylindrical transparent polymethylpentene jar
(Nalgene, Thermo Fisher Scientific, NY, USA) with dried and CO2-
free air flowing through at the rate of 1lmin–1. The temperature
inside the jar was measured using an NTC thermistor (Sable
Systems, Las Vegas, NV, USA). The measurements were performed
in two separate temperature-controlled rooms. Magnesium
perchlorate was used to dry the air before it entered the Li-Cor 6251
CO2analyser (Li-Cor Biosciences, Lincoln, NE, USA). The analyser
was calibrated against two gas mixtures before the experiment: one
with no CO2and the other with a concentration corresponding to
the highest levels produced by the butterflies. During the experiment
the analyser was calibrated against the zero gas two to three times
a day. The program ExpeData (Sable Systems) was used to record
The experimental setup did not allow detection of detailed patterns
in gas exchange during the resting stage, rather it gave a smoothed
average. Measurements of resting metabolic rate with better temporal
resolution have however shown no signs of discontinuous gas
exchange in the Glanville fritillary (K.N., unpublished data). RMR
appeared stable over long periods and rare events of activity inside
the darkened chamber could be detected as rise in the CO2curve.
If an individual became active before the curve had stabilised to a
steady baseline, additional time was allowed for the butterfly to settle
to complete rest. RMR was calculated as the mean CO2emission
rate during a period of 40s of stable CO2production. Half of the
RMR measurements were performed at 26°C (s.d.=0.82,
min.=23.3°C, max.=26.8°C), and the other half at 32°C (s.d.=0.73,
Following the measurement of RMR, the black cloth was
removed and the butterfly was stimulated to fly as continuously as
possible by shaking and tapping the jar for 10min. The butterfly
was thereby repeatedly forced to fly as soon as it attempted to land
on the walls of the respirometry jar. Individuals typically flew more
or less continuously for the first 3min, after which the length of
flight bouts became more variable. Some individuals showed clear
signs of fatigue towards the end of the experiment, whereas others
flew for the full 10 min period. The measurement temperature
remained relatively stable during the measurement. The average
temperature at the moment of the highest CO2 production was
26.7°C (s.d.=0.53, min.=25.0°C, max.=27.4°C) in the lower
temperature treatment, and 32.3°C (s.d.=0.55, min.=31.0°C,
max.=33.0°C) in the higher temperature treatment.
Experiment on pupal and adult resting metabolic rates and
flight metabolic rate in a range of temperatures
In the second experiment butterflies originating from the Åland
Islands, Estonia and China were used. All larvae were reared in
common garden conditions starting from winter diapause. Individuals
from Åland were the offspring of butterflies collected in the field as
larvae, reared in the laboratory and mated in an outdoor cage.
Estonian and Chinese individuals were collected as larvae in the
respective field sites in the previous autumn. All larvae were reared
in growth chambers (16 h:8 h L:D). The ambient temperature
followed a stepwise programme with a maximum of 28°C at mid-
day and 12°C in the night. Larvae were fed ad libitum with leaves
of Plantago lanceolata. There were no differences in the metabolic
rates of butterflies originating from the different source populations,
and results for the pooled material are therefore reported in this study.
The metabolic rate of each individual was measured twice in this
experiment: first as a pupa (on average 7.5 days before eclosion)
and then as an adult on the second full day after eclosion. A pupa
was gently placed in a 7ml respirometry chamber with dried and
CO2-free air flowing through at the rate of 480 ml min–1. The
respirometer was kept in darkness during the measurement. The
individuals recovered quickly from the handling and showed stable
metabolic rates after the first 2 min. The gas exchange was
continuous and fluctuated around a stable mean value. Individuals
did not generally show signs of closing their spiracles fully for any
extended periods. Pupal metabolic rate (MRpupa) was calculated as
the average CO2 production of the last 5 min of the 10 min
measurement period. The average measurement temperature was
25.3°C (s.d.=0.53, min.=23.9°C, max.=26.8°C). The pupae were
stored in individual plastic cups with a moist piece of paper prior
to and after the measurements.
After eclosion, butterflies were marked and weighed. Marked
butterflies were moved to a mesh cage and left in shade. On the
following day butterflies had flight practice in a cage exposed to
sunlight for approximately 1 h. The measurements on adult
butterflies were done on the second day after eclosion. The
measurements of RMR and MRpeakwere performed as in the first
experiment (above). The respirometry equipment was operated in
THE JOURNAL OF EXPERIMENTAL BIOLOGY
a large insulated plywood chamber that was heated with an electric
heater and equipped with a fan to stabilise the temperature. The
average temperatures inside the respirometry jar were 32.5°C
(s.d.=1.28, min.=28.9°C, max.=35.0°C) during the measurements
of RMR, and 32.9°C (s.d.=0.90, min.=30.8°C, max.=35.1°C) during
the measurements of MRpeak.
DNA was extracted from the wing samples taken during the
marking of butterflies on the day of eclosion (see above). The Pgi
genotype was characterised as a single nucleotide polymorphism
(SNP) in the coding region of the Pgi gene, using the methods
described for this species (Orsini et al., 2009).
Factors affecting metabolic rate in the different experiments were
analysed with ANCOVA using Proc Mixed in SAS 9.1. Backward
selection was used to eliminate clearly nonsignificant factors
(P>0.10) from the initial model that contained all two-way
interactions and squared terms. A nonsignificant main effect was
retained in the model if an interaction containing that term was
significant. When the model contained quadratic terms type I sum
of squares were used.
The effect of temperature on RMR was calculated as the Q10
value, indicating the increase in metabolic rate with an increase in
temperature of 10°C. In the first experiment Q10was calculated from
the two temperature treatments using the average RMR for the
respective treatments. In the second experiment, a linear regression
was used to obtain the predicted RMR values from both ends of
the temperature range and these values were used to calculate Q10.
The three possible base pair combinations (genotypes) at SNP
AA111 are AA, AC and CC. The AA111 AC and CC genotypes
correspond closely to the f-allele in previous allozyme studies (Haag
et al., 2005; Saastamoinen, 2007). The frequency of the CC
genotype is very low in the Åland metapopulation (Orsini et al.,
2009), hence no CC homozygotes were found in the first experiment.
With one exception, the CC homozygotes in the second experiment
were Chinese and Estonian individuals. The reason for the very low
frequency of the CC homozygotes in the Åland population is unclear,
but it may be related to linkage with a deleterious recessive
mutation (Orsini et al., 2009).
In the first experiment, the resting metabolic rate (RMR) was
influenced by body mass and measurement temperature (Table1).
RMR for a given body mass was about twice as high at 32°C as at
26°C (Fig.1A), yielding the Q10value of 2.6. Neither time of the
day nor Pgi genotype had any significant effect on RMR, nor were
any interactions significant (Table1).
The peak flight metabolic rate (MRpeak) was positively correlated
with body mass but the measurement temperature had only a weak
and statistically nonsignificant effect on MRpeak(Table1). Instead,
there was a strong effect of the Pgi genotype on MRpeakin both
temperature treatments. An average-sized AC heterozygote had
~45% higher MRpeakthan an AA homozygote (Fig.1B). The effect
of Pgi became stronger with increasing body mass, as indicated by
the near-significant genotype by body mass interaction (Table1).
In the second experiment, the pupal metabolic rate (MRpupa) was
influenced by the mass of the eclosed butterfly, temperature and a
nonlinear time effect with a peak in the early afternoon (Fig.2A–C,
Table2). Pgi genotype had no significant main effect, though there
was a weak interaction suggesting that AC and CC individuals may
have higher pupal metabolic rates in low temperatures than AA
individuals (Fig.2). Adult RMR was affected by the same factors
as MRpupa: body mass, temperature and time of the day (Fig.2D–F,
Table 2), but Pgi genotype had no effect on RMR. The Q10
calculated for the range of temperatures from 29°C to 35°C was
Table 1. Factors affecting resting metabolic rate and peak flight metabolic rate
Mass ? Pgi
RMR, resting metabolic rate; MRpeak, flight metabolic rate.
Each individual was measured either at 26°C or at 32°C.
Bold indicates significance.
Body mass (mg)
60 80 100 120 140 160
RMR (ml CO2 h–1)
60 80 100 120 140 160
MRpeak (ml CO2 h–1)
Fig.1. (A)Resting metabolic rate (RMR) of adult
butterflies measured at 26°C and at 32°C. The
horizontal axis gives the wet adult body mass. Pgi
genotype had no effect on RMR. (B)Peak flight
metabolic rate (MRpeak) plotted against wet body
mass. Grey squares and the dashed line represent
Pgi AA111 AA homozygotes, black dots and the
solid line Pgi AA111 AC heterozygotes. The
temperature treatment had no significant effect.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
1045 Pgi effects on butterfly metabolic rate
The peak flight metabolic rate was not affected by the body mass
of the adult butterfly (Table2). There was a positive main effect of
measurement temperature but no significant Pgi genotype main
effect. However, there was a highly significant genotype ?
temperature interaction due to a linear increase of MRpeakwith
temperature in AA homozygotes, whereas in AC heterozygotes the
highest metabolic rates were reached in low measurement
temperatures (Fig.2H). CC homozygotes showed an intermediate
response to temperature. MRpeakshowed a nonlinear trend with the
time of the day, but this effect was not statistically significant.
To examine the consistency of the genotypic effect on MRpeak
in different temperatures the data from the two experiments were
pooled. Estonian and Chinese individuals were omitted from this
dataset because the molecular structure of the Pgi gene may differ
among the populations. Adult mass had a clear effect on MRpeak
(F1,105=11.81, P=0.0008). AC heterozygotes were metabolically
superior over a broad range of temperatures (F1,105=17.04, P<0.0001;
Fig.3). Temperature had a marginal effect in the dataset (F1,105=3.81,
P=0.0536), but the genotype–temperature interaction was evident
in the higher measurement temperatures as explained above
The uncorrected RMR and MRpeakwere weakly correlated in the
two experiments, largely due to both being dependent on body size.
To take into account the effects of body size, measurement
temperature, time of the day and Pgi genotype and its interaction
with temperature, residuals from the respective models were used.
No significant correlation between the residual RMR and residual
MRpeak was found in the first experiment, even when the two
temperature treatments were analysed separately. In the second
experiment there was a significant positive correlation (r=0.3129,
n=73, P=0.007), with linear regression explaining 9.8% of the
variance in residual MRpeak.
Effects of body mass, temperature, Pgi genotype and their
The body mass of an individual was positively correlated with its
metabolic rate, in agreement with the existing literature (Chown and
Nicolson, 2004; Kleiber, 1947; Schmidt-Nielsen, 1984). The
relationship between body mass and metabolic rate is one of the most
fundamental biological generalisations, though it also remains
somewhat controversial (Chown et al., 2007; Downs et al., 2008;
Niven and Scharlemann, 2005; Suarez et al., 2004). The present results
contribute an interesting twist to the quest for a common scaling factor
of metabolic rate by body mass: here the relationship between MRpeak
and body mass appears to be genotype specific, with a steeper slope
for the AC heterozygotes than for the AA homozygotes (Fig.1B).
Most of the literature deals with the relationship between resting
metabolic rate and body mass, while measurements of maximum
metabolic rate are less common. The present finding suggests that
the factors affecting the maximum metabolic rate are more complex
than those affecting the resting metabolic rate.
Unexpectedly, adult body mass had no effect on MRpeakin the
second experiment. This result is most likely due to variation in
adult mass, which was measured on the day of eclosion. Newly
eclosed individuals vary in the rate of meconium clearance, and
some individuals may therefore have weighed more than what their
actual body mass was during the measurement of the metabolic rate.
A subset of individuals was weighed after the measurement and
among these individuals a significant positive correlation between
MRpeakand post-measurement body mass was evident (t57=2.37,
P=0.021). Because the butterflies were used for other purposes
following the experiment, no dry mass or masses of separate body
parts were obtained.
As expected, temperature had clear effects on metabolic rates,
especially on RMR. The Q10values for RMR in the first and the
second experiment were 2.6 and 2.1, respectively. These values
indicate that temperature dependence of RMR in the Glanville
fritillary is comparable with that in other insects (Chown and
Nicolson, 2004; Downs et al., 2008).
The effect of temperature on MRpeakwas different from that on
RMR. In the first experiment, there was no significant difference
in MRpeakbetween the two temperature treatments. It thus appears
that the flight metabolic rate is essentially independent of
temperature between 26 and 32°C. However, in the second
experiment, when the measurement temperatures exceeded 33°C,
temperature had a genotype-specific effect on MRpeak(Fig.3). In
the range from 31 to 35°C, Pgi AA homozygotes showed a positive
relationship between MRpeak and temperature, whereas in AC
heterozygotes the relationship was negative. In other words, AC
heterozygotes are the metabolically superior genotype in low to
moderate temperatures, but AA homozygotes perform better in the
highest temperatures. The effect was the same in the full dataset
consisting of Finnish, Estonian and Chinese butterflies (Fig.2H)
and in the subset of Finnish butterflies (Fig.3).
A similar interaction between Pgi genotype and temperature has
previously been reported for flight metabolism and dispersal rate
in the field in the Glanville fritillary (Niitepõld et al., 2009). Both
Pgi genotypes (the AA homozyotes and the AC heterozygotes)
appear to follow a nonlinear though dissimilar reaction norm of
metabolic rate in relation to temperature. Such a pattern may arise
from biochemical, biomechanical or behavioural reasons, or as a
combination of all of them (Harrison and Roberts, 2000). Inverted
U-shaped relationships between temperature and performance have
been reported in the power output of flight muscles of a winter-
flying moth (Marden, 1995a) and in the force production of tethered
honeybees (Coelho, 1991) and dragonflies (Marden, 1995b). In
honeybees, flight metabolic rate seems to be lower in high ambient
temperatures than in moderate temperatures (Harrison and Fewell,
2002; Harrison et al., 1996; Woods et al., 2005).
Table 2. Factors affecting pupal metabolic rate, adult resting metabolic rate and adult peak metabolic rate in flight
Temp ? Pgi
F1,64=4.71; P= =0.0336
F1,64=8.23; P= =0.0056
F1,64=7.71; P= =0.0072
F1,64=5.42; P= =0.0231
F1,66=4.20; P= =0.0446
F1,66=10.52; P= =0.0019
F1,64=5.46; P= =0.0065
MRpupa, pupal metabolic rate; RMR, adult resting metabolic rate; MRpeak, adult peak flight metabolic rate.
The measurement temperatures ranged from 24°C to 27°C in pupae, from 29°C to 35°C in resting adults, and from 31°C to 35°C in flying adults.
Bold indicates significance.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
Individuals with different variants of the PGI enzyme have been
shown to differ in thermal sensitivity in North American Colias
butterflies (Watt, 1977; Watt et al., 2003) and in willow beetles
(Dahlhoff and Rank, 2000; Dahlhoff and Rank, 2007). The differences
in organismal performance have been traced to variation in enzyme
kinetics and thermal stability: homozygous PGI enzyme variants with
high kinetics are sensitive to temperature, whereas variants with low
activity are thermally stable (Watt, 1977). Heterozygous enzyme
variants combine high activity with good thermal stability. In the
present study, in the range from 26 to 33°C, temperature had a very
limited effect on flight metabolism but the difference between the
Pgi genotypes was highly significant. This temperature range is
relevant for northern temperate butterflies, which live in low ambient
temperatures and are hence dependent on solar radiation to increase
their body temperature. The actual body or thorax temperatures were
not measured in this study, but in the beginning of the measurement
the thorax temperature must have corresponded closely to the ambient
The measurement temperatures in the present study match with
thoracic surface temperatures of female Glanville fritillaries caught
in flight in the field (Saastamoinen and Hanski, 2008). Over a range
of typical ambient temperatures the thorax surface temperature,
recorded using a thermal image camera, was on average 30.1°C,
rarely higher than 33°C, and maximally 35°C (Saastamoinen and
Hanski, 2008). I therefore conclude that under natural
environmental conditions the PgiAC individuals benefit from their
higher flight metabolic rate compared with AA homozygotes. On
hot days the relationship may be reversed, but even then
heterozygotes may benefit by being able to initiate activity earlier
in the day when temperatures are still lower (Saastamoinen and
In resting butterflies the metabolic rate increased linearly with
temperature. Pgihad no effect on RMR and there was no interaction
with temperature. These results suggest that the effect of Pgi
genotype is due to restrictions on the maximum capacity of the
enzyme function. At rest, all metabolic pathways function at very
9 11 13 15 17 19
80 100 120 140
28 30 32 34 36
9 11 13 15 17 19
80 100 120 140
23 24 25 26 27
Adult mass (Mg)
80 100 120 140
Measurement temperature (°C)
30 31 32 33 34 35 36
9 11 13 15 17 19
RMR (ml CO2 h–1)
MRpeak (ml CO2 h–1)
MRpupa (ml CO2 h–1)
(adjusted with adult mass)
(adjusted with mass)
(adjusted with mass)
(adjusted with mass and temp)
(adjusted with adult mass,
temp, Pgi and Pgi ? temp)
(adjusted with mass, temp,
Pgi and Pgi ? temp)
Fig.2. Effects of adult body mass, measurement temperature, time of measurement and Pgi genotype on pupal metabolic rate (A–C), adult resting metabolic
rate (D–F) and peak flight metabolic rate (G-I). (A,D,G) The rate of CO2emission plotted against body mass. (B,E,H) Residual mass-independent metabolic
rates plotted against measurement temperature. B and H show interactions between Pgi genotype and temperature. Grey squares and the long dashed line
represent the genotype AA, black circles and the solid line represent AC, and white triangles and the short dashed line represent the CC genotype. (C,F,I) The
effect of the time of day on residual metabolic rates (adjusted for mass, temperature and, when significant, for the genotype by temperature interaction).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
1047Pgi effects on butterfly metabolic rate
low and stable rates and hence no differences between the enzyme
forms are likely to occur (Watt and Dean, 2000).
The metabolic rate of pupae increased with temperature. The
absolute values of MRpupawere directly comparable with resting
metabolic rates of adult butterflies measured in similar temperatures
(Fig.1A, Fig.2A). Contrary to RMR, MRpupaappeared to be affected
by an interaction between temperature and Pgi genotype. The
interaction, though not quite significant at 5% level, resembled the
one found for MRpeak, with a linear increase in metabolic rate with
temperature in AA homozygotes but no increase with temperature
in AC heterozygotes (Fig.2B).
Dahlhoff et al. (Dahlhoff et al., 2008) have reported a significant
PGI ? temperature interaction in the RMR of the willow beetle
Chrysomela aeneicollis. In their experiment, individuals were
measured at two temperatures, 20°C and 36°C. At the lower
temperature there were no significant differences between the PGI
genotypes (electromorphs), but the homozygotes that are most
commonly found in warm environments had slightly lower
metabolic rates than the alternative homozygotes and the
heterozygotes. In the higher temperature, close to the field-measured
maximum, the metabolic rate of the warm-environment specialist
genotype was significantly higher than those of the two other
genotypes (Dahlhoff et al., 2008). These results suggest that one
genotype is indeed metabolically adapted to warm environments,
while the others may perform better at lower temperatures.
The time of the day influenced metabolic rates. Individuals
reached the highest metabolic rates during early afternoon. This
effect was strongest in pupae but the pattern was similar also in
adults. Diel patterns in resting metabolic rates have previously been
reported in other Lepidopteran species, both in pupae (Crozier, 1979)
and adult individuals (Canzano et al., 2006). In the first experiment
in this study, time of the day had only a weak positive and
statistically nonsignificant effect on RMR and no effect on MRpeak.
This may be due to conditions experienced by the individuals prior
to the measurements, perhaps already during the larval stages. The
rearing conditions were different in the two experiments, and the
diel cycle was more natural in the second experiment.
Correlations between resting and flight metabolic rates
Basal metabolic rate (BMR) and resting or standard metabolic rates
are among the most commonly recorded physiological variables in
animals. In mammals and birds, basal metabolic rate represents the
maintenance cost of the physiological machinery and correlates with
the maximum metabolic capacity (White and Seymour, 2004). BMR
can therefore be used as an indicator of metabolic capacity and it
has been demonstrated to vary according to conditions requiring
high metabolic capacity and endurance (Broggi et al., 2007; Nilsson,
2002). To what extent are different levels of metabolism correlated
in the Glanville fritillary butterfly?
The relationship between RMR and MRpeakwas examined under
three conditions: at low temperature, at optimal temperature, and
across a range of temperatures. In the first experiment with two
treatments, both RMR and MRpeakwere positively correlated with
body mass but there was no mass-independent correlation between
RMR and MRpeak. The resting metabolic rate had thus no predictive
value for the mass-independent metabolic rate in flight. In the second
experiment there was a significant positive mass-independent
correlation between RMR and MRpeak, though the correlation
explained less than 10% of variation in MRpeak. MRpupadid not
correlate at all with MRpeak, and the correlation between MRpupa
and RMR was weak and clearly nonsignificant.
The poor predictive power of resting metabolic rates for maximum
metabolic rate may partly be due to measurement error and
measurements in different temperatures; more controlled conditions
could have yielded more reliable results. However, a correlation
should be robust if it is to be used for predictive purposes. Clearly,
measuring directly the flight metabolic rate rather than the RMR is
necessary when one is interested in flight-related processes in this
and probably other butterflies.
The absence of clear correlation between RMR and MRpeak
implies only a small general cost of high metabolic capacity. This
means that individual butterflies can invest in flight ability and reach
high levels of energy expenditure in flight without having to maintain
a high metabolic rate at rest. In mammals and birds such a cost
seems to be greater, as high maximum metabolic capacity is
reflected in high resting metabolic rate (Bennett and Ruben, 1979;
Dutenhoffer and Swanson, 1996).
The ratio of flight metabolic rate over resting metabolic rate was
relatively small in this study. Flying insects can show a 100-fold
or even greater increase in metabolic rate (Bartholomew and Casey,
1978; Chown and Nicolson, 2004), whereas in this study the average
increase was 13- to 30-fold. The scale of the increase was negatively
affected by temperature: in low temperatures the increase was greater
due to the strong dependence of RMR on temperature. The highest
increases in metabolic rate from rest to flight were 59-fold at 26°C
and 49-fold at 32°C in the first experiment, and 21-fold in the second
experiment, measured at 32.4°C. The low increase in the second
experiment was caused by the high levels of RMR; there were no
differences in MRpeakamong the experiments. The high levels of
RMR may be due to higher physiological activity possibly
influenced by the different conditions under which butterflies were
maintained in the two experiments.
The values reported here are not fully comparable with factorial
scopes reported in the literature because the measurement
temperatures were high in order to enable flight. Endothermic
insects, such as hawkmoths (Bartholomew and Casey, 1978), can
be measured at lower temperatures than butterflies, thus resulting
in higher factorial scopes. MRpeakwas not corrected for the time
lag in CO2being transported from the respirometer to the analyser,
which lowers the measured peak value (Bartholomew et al., 1981).
Measurement temperature (°C)
25 27 29 31 33 35
(adjusted with body mass)
Fig.3. Peak flight metabolic rate (adjusted for body mass) plotted against
measurement temperature in a pooled dataset containing the Finnish
individuals from the two experiments. Grey squares and the dashed lines
represent AA111 AA homozygotes, and black dots with the solid lines
represent the AC heterozygotes. The long regression lines were fitted to
the full dataset, while the short regression lines were fitted only to the data
for higher temperatures and show an interaction between genotype and
temperature (F1,69=10.31, P=0.002).
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1048 K. Niitepõld Download full-text
Methodological issues are unlikely to be the only reasons for the
relatively low increase in metabolic rate in the present experiment.
It seems reasonable to conclude that flight is relatively cheap in
butterflies with large wing areas, low wing loadings and low
wingbeat frequency compared with other insects with narrower
wings, higher wing loadings and higher wingbeat frequency.
Measurements of other butterfly species such as the powerful fliers
Inochis io and Vanessa cardui have also yielded only 10- to 20-
fold increase in metabolic rate (K.N., unpublished data), suggesting
that the present results for the Glanville fritillary may be general
for butterflies and other insects with low wingbeat frequency.
LIST OF SYMBOLS AND ABBREVIATIONS
basal metabolic rate (the minimum metabolic rate of
peak metabolic rate
pupal metabolic rate
resting metabolic rate (the temperature-dependent minimum
metabolic rate of ectothermic animals)
single nucleotide polymorphism
I thank Marjo Saastamoinen who provided help with the first experiment. I am
grateful to Jim Marden and Ilkka Hanski for their support and comments. Three
anonymous referees provided useful comments and helped to improve the
manuscript. Chris Wheat, Virpi Ahola, Eliezer Gurarie and Phil Harrison are
thanked for fruitful discussions. Toshka Nyman and Luisa Orsini helped with
genotyping. The work was funded by the Academy of Finland (Finnish Centre of
Excellence Programme, grants numbers 38604 and 44887) and the U.S. National
Science Foundation (EF-0412651).
Bartholomew, G. A. and Casey, T. M. (1978). Oxygen-consumption of moths during
rest, pre-flight warm-up, and flight in relation to body size and wing morphology. J.
Exp. Biol. 76, 11-25.
Bartholomew, G. A., Vleck, D. and Vleck, C. M. (1981). Instantaneous
measurements of oxygen-consumption during pre-flight warm-up and post-flight
cooling in sphingid and saturniid moths. J. Exp. Biol. 90, 17-32.
Bennett, A. F. and Ruben, J. A. (1979). Endothermy and activity in vertebrates.
Science 206, 649-654.
Broggi, J., Hohtola, E., Koivula, K., Orell, M., Thomson, R. L. and Nilsson, J.-Å.
(2007). Sources of variation in winter basal metabolic rate in the great tit. Funct.
Ecol. 21, 528-533.
Canzano, A. A., Krockenberger, A. A., Jones, R. E. and Seymour, J. E. (2006).
Rates of metabolism in diapausing and reproductively active tropical butterflies,
Euploea core and Euploea sylvester (Lepidoptera: Nymphalidae). Physiol. Entomol.
Chown, S. L. and Nicolson, S. W. (2004). Insect Physiological Ecology: Mechanisms
and Patterns. Oxford: Oxford University Press.
Chown, S. L., Marais, E., Terblanche, J. S., Klok, C. J., Lighton, J. R. B. and
Blackburn, T. M. (2007). Scaling of insect metabolic rate is inconsistent with the
nutrient supply network model. Funct. Ecol. 21, 282-290.
Coelho, J. R. (1991). The effect of thorax temperature on force production during
tethered flight in honeybee (Apis mellifera) drones, workers, and queens. Physiol.
Zool. 64, 823-835.
Coelho, J. R. and Mitton, J. B. (1988). Oxygen consumption during hovering is
associated with genetic variation of enzymes in honey-bees. Funct. Ecol. 2, 141-146.
Crozier, A. J. G. (1979). Diel oxygen-uptake rhythms in diapausing pupae of Pieris
brassicae and Papilio machaon. J. Insect Physiol. 25, 647-652.
Dahlhoff, E. P. and Rank, N. E. (2000). Functional and physiological consequences of
genetic variation at phosphoglucose isomerase: Heat shock protein expression is
related to enzyme genotype in a montane beetle. Proc. Natl. Acad. Sci. USA 97,
Dahlhoff, E. P. and Rank, N. E. (2007). The role of stress proteins in responses of a
montane willow leaf beetle to environmental temperature variation. J. Biosci. 32,
Dahlhoff, E. P., Fearnley, S. L., Bruce, D. A., Gibbs, A. G., Stoneking, R.,
McMillan, D. M., Deiner, K., Smiley, J. T. and Rank, N. E. (2008). Effects of
temperature on physiology and reproductive success of a montane leaf beetle:
Implications for persistence of native populations enduring climate change. Physiol.
Biochem. Zool. 81, 718-732.
Downs, C. J., Hayes, J. P. and Tracy, C. R. (2008). Scaling metabolic rate with body
mass and inverse body temperature: a test of the Arrhenius fractal supply model.
Funct. Ecol. 22, 239-244.
Dutenhoffer, M. S. and Swanson, D. L. (1996). Relationship of basal to summit
metabolic rate in passerine birds and the aerobic capacity model for the evolution of
endothermy. Physiol. Zool. 69, 1232-1254.
Ehrlich, P. R. and Hanski, I. (2004). On the Wings of Checkerpots: A Model System
for Population Biology (ed. P. R. Ehrlich and I. Hanski). Oxford: Oxford University
Ellegren, H. and Sheldon, B. C. (2008). Genetic basis of fitness differences in natural
populations. Nature 452, 169-175.
Haag, C. R., Saastamoinen, M., Marden, J. H. and Hanski, I. (2005). A candidate
locus for variation in dispersal rate in a butterfly metapopulation. Proc. R. Soc.
London B Biol. Sci. 272, 2449-2456.
Hanski, I. (1999). Metapopulation Ecology. New York: Oxford University Press.
Hanski, I. and Ovaskainen, O. (2000). The metapopulation capacity of a fragmented
landscape. Nature 404, 755-758.
Harrison, J. F. and Fewell, J. H. (2002). Environmental and genetic influences on
flight metabolic rate in the honey bee, Apis mellifera. Comp. Biochem. Physiol.
Harrison, J. F. and Roberts, S. P. (2000). Flight respiration and energetics. Annu.
Rev. Physiol. 62, 179-205.
Harrison, J. F., Nielsen, D. I. and Page, R. E. (1996). Malate dehydrogenase
phenotype, temperature and colony effects on flight metabolic rate in the honey-bee,
Apis mellifera. Funct. Ecol. 10, 81-88.
Hinds, D. S., Baudinette, R. V., MacMillen, R. E. and Halpern, E. A. (1993).
Maximum metabolism and the aerobic factorial scope of endotherms. J. Exp. Biol.
Kleiber, M. (1947). Body size and metabolic rate. Physiol. Zool. 27, 511-541.
Laurie-Ahlberg, C. C., Barnes, P. T., Curtsinger, J. W., Emigh, T. H., Karlin, B.,
Morris, R., Norman, R. A. and Wilton, A. N. (1985). Genetic variability of flight
metabolism in Drosophila melanogaster. II. Relationship between power output and
enzyme activity levels. Genetics 111, 845-868.
Marden, J. (1995a). Evolutionary adaptation of contractile performance in muscle of
ectothermic winter-flying moths. J. Exp. Biol. 198, 2087-2094.
Marden, J. (1995b). Large-scale changes in thermal sensitivity of flight performance
during adult maturation in a dragonfly. J. Exp. Biol. 198, 2095-2102.
Montooth, K. L., Marden, J. H. and Clark, A. G. (2003). Mapping determinants of
variation in energy metabolism, respiration and flight in Drosophila. Genetics 165,
Nespolo, R. F., Castaneda, L. E. and Roff, D. A. (2007). Quantitative genetic
variation of metabolism in the nymphs of the sand cricket, Gryllus firmus, inferred
from an analysis of inbred-lines. Biol. Res. 40, 5-12.
Nespolo, R. F., Roff, D. A. and Fairbairn, D. J. (2008). Energetic trade-off between
maintenance costs and flight capacity in the sand cricket (Gryllus firmus). Funct.
Ecol. 22, 624-631.
Niitepõld, K., Smith, A. D., Osborne, J. L., Reynolds, D. R., Carreck, N. L., Martin,
A. P., Marden, J. H., Ovaskainen, O. and Hanski, I. (2009). Flight metabolic rate
and Pgi genotype influence butterfly dispersal rate in the field. Ecology 90, 2223-
Nilsson, J.-Å. (2002). Metabolic consequences of hard work. Proc. R. Soc. Lond. B
Biol. Sci. 269, 1735-1739.
Niven, J. E. and Scharlemann, J. P. W. (2005). Do insect metabolic rates at rest and
during flight scale with body mass? Biol. Lett. 1, 346-349.
Orsini, L., Wheat, C. W., Haag, C. R., Kvist, J., Frilander, M. J. and Hanski, I.
(2009). Fitness differences associated with Pgi SNP genotypes in the Glanville
fritillary butterfly (Melitaea cinxia). J. Evol. Biol. 22, 367-375.
Reinhold, K. (1999). Energetically costly behaviour and the evolution of resting
metabolic rate in insects. Funct. Ecol. 13, 217-224.
Saastamoinen, M. (2007). Life-history, genotypic, and environmental correlates of
clutch size in the Glanville fritillary butterfly. Ecol. Entomol. 32, 235-242.
Saastamoinen, M. and Hanski, I. (2008). Genotypic and environmental effects on
flight activity and oviposition in the Glanville fritillary butterfly. Am. Nat. 171, E701-
Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P., Fortelius, W. and Hanski, I.
(1998). Inbreeding and extinction in a butterfly metapopulation. Nature 392, 491-494.
Saglam, I. K., Roff, D. A. and Fairbairn, D. J. (2008). Male sand crickets trade-off
flight capability for reproductive potential. J. Evol. Biol. 21, 997-1004.
Schmidt-Nielsen, K. (1984). Scaling: Why is Animal Size so Important. Cambridge:
Cambridge University Press.
Suarez, R. K., Darveau, C. A. and Childress, J. J. (2004). Metabolic scaling: a many-
splendoured thing. Comp. Bioch. Physiol. 139B, 531-541.
Tolman, T. and Levington, R. (1997). Butterflies of Britain and Europe. London:
Vera, J. C., Wheat, C. W., Fescemyer, H. W., Frilander, M. J., Crawford, D. L.,
Hanski, I. and Marden, J. H. (2008). Rapid transcriptome characterization for a
nonmodel organism using 454 pyrosequencing. Molec. Ecol. 17, 1636-1647.
Walton, B. M. (1993). Physiology and phylogeny: The evolution of locomotor
energetics in hylid frogs. Am. Nat. 141, 26-50.
Watt, W. B. (1977). Adaptation at specific loci. I. Natural selection on phosphoglucose
isomerase of Colias butterflies – biochemical and population aspects. Genetics 87,
Watt, W. B. (1983). Adaptation at specific loci. II. Demographic and biochemical
elements in the maintenance of the Colias PGI polymorphism. Genetics 103, 691-
Watt, W. B. and Dean, A. M. (2000). Molecular-functional studies of adaptive genetic
variation in prokaryotes and eukaryotes. Annu. Rev. Genet. 34, 593-622.
Watt, W. B., Wheat, C. W., Meyer, E. H. and Martin, J. F. (2003). Adaptation at
specific loci. VII. Natural selection, dispersal and the diversity of molecular-functional
variation patterns among butterfly species complexes (Colias: Lepidoptera, Pieridae).
Mol. Ecol. 12, 1265-1275.
White, C. R. and Seymour, R. S. (2004). Does basal metabolic rate contain a useful
signal? Mammalian BMR allometry and correlations with a selection of physiological,
ecological, and life-history variables. Physiol. Biochem. Zool. 77, 929-941.
Woods, W. A., Heinrich, B. and Stevenson, R. D. (2005). Honeybee flight metabolic
rate: does it depend upon air temperature? J. Exp. Biol. 208, 1161-1173.
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