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First published online January 18, 2008
Journal of Experimental Biology 211, 391-400 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.013169
Scaling of metabolism in Helix aspersa snails: changes through ontogeny and response to selection for increased size
ski1,*owski1
1 Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7,
30-387 Kraków, Poland
2 INRA, UR 544, Unité de Génétique des Poissons, F-78350
Jouy en Josas, France
3 INRA, Unité Héliciculture, BP 52, F-17700 Surg
res,
France
4 ENSAR, Direction Scientifique, F-35042 Rennes Cedex, France
* Author for correspondence (e-mail: marcin.czarnoleski{at}uj.edu.pl)
Accepted 20 November 2007
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: metabolism, growth rate, 3/4 power law, life history, allometry, cost of growth, body size, experimental evolution, growth efficiency, bioenergetics, food consumption, cell size, Bertalanffy's theory, metabolic theory
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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; Sibly and Calow,
1986; Konarzewski,
1995; Kooijman,
2000; Lochmiller and
Deerenberg, 2000; Furness,
2003; Kozowski et al.,
2003; Martin et al.,
2003; Radwan et al.,
2006). It is not surprising, then, that Rubner's
(Rubner, 1883) discovery of
the non-isometric increase of the metabolic rate with body size in dogs
spurred a debate on the origin of size-scaling of metabolism
(von Bertalanffy, 1951;
McMahon, 1973;
Schmidt-Nielsen, 1984;
Sibly and Calow, 1986;
West et al., 1997;
Kooijman, 2000;
Kozowski et al., 2003;
Glazier, 2006). The dispute
has centered around two questions: what are the universal patterns in the
size-scaling of metabolism, and how are the scalings to be explained
(Glazier, 2005). Metabolic
rate (
O2) is
usually assumed to be a power function of body mass (Mb),
 | (1) |
;
von Bertalanffy, 1951;
von Bertalanffy, 1957;
Heusner, 1982;
Peters, 1983;
Brown et al., 1997); models and
hypotheses considering various biophysical constraints have been formulated to
validate such exponent values theoretically
(Glazier, 2005). However, the
universality of 2/3 and 3/4 metabolic scaling has been questioned recently
(e.g. Kozowski et al.,
2003; Glazier,
2005; Chown et al.,
2007). Glazier's analysis of published data shows that variability
of metabolic scaling and deviations from the 2/3 and 3/4 scaling modes are
widespread in nature: the value of exponent b ranges from 0.38 to 1.11 in
mammals, from 0.27 to 1.26 in squamate reptiles, and from –1.2 to 2.05
in invertebrates; among 642 intraspecific exponent values analyzed by Glazier,
45.8% differ significantly from b=2/3 and 50.2% deviate from b=3/4. It is
important to identify the factors diversifying exponent b, not only to answer
whether there are universal modes of metabolic scaling, but also to achieve a
fuller understanding of phenotypic variability in nature. Metabolic scaling is
expected to affect optimal resource allocation to growth and reproduction, so
it should substantially influence the life history of organisms
(Kozowski and Teriokhin,
1999; Czarnoski
et al., 2003). Emerging evidence suggests that adaptive allocation
responses to shifts in metabolic scaling can explain different ecological and
evolutionary phenomena, such as the so-called `temperature-size rule' in
ectotherms (slower growth and larger final body size in colder environments)
(Angilletta and Dunham, 2003;
Kozowski et al.,
2004), or the interspecific patterns of body size distributions
and life history allometries
(Kozowski and Weiner,
1997; Kindlemann et al.,
1999; Kozowski and
Gawelczyk, 2002).
In this work we examine the link between size-scaling of metabolism and
growth rate in Helix aspersa snails. We analyze shifts of metabolic
scaling across snail ontogeny and compare scaling of metabolism in normal
snails and snails artificially selected for increased adult size. First we
provide a physiological and life history background for metabolic analysis by
analyzing correlated responses of growth traits, food consumption and snail
viability to selection. Then we test the hypothesis that metabolic scaling is
steeper in fast-growing than in slow-growing organisms
(Riisgård, 1998;
Glazier, 2005) by comparing
metabolic exponent b in fast- versus slow-growth phases of snail
ontogeny, and in slow- versus fast-growing genetic lines of snails.
Finally, we examine whether the metabolic scalings conform to the 2/3 and 3/4
power laws.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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)].
After three generations of random breeding, control and selection lines were
derived (Dupont-Nivet et al.,
2000). The control was maintained by breeding individuals
randomly; each generation was produced from at least 30 families to maintain
genetic variability. The selected line was produced by applying individual
selection for increased adult mass; the proportion of selected animals was
13% for the first five generations and 30% after that. The
individuals used in this study came from the seventh generation of both lines
(tenth generation after the establishment of the snail stock). In the spring
of 2002, 20 egg-layings per line were randomly taken from different parents
(families). After hatching, 60 newborns were randomly chosen from each clutch
and reared for 5 months. Each family group was kept in a wooden box
(120x300x480 mm) lined with moist moss and covered with a
plastic-mesh lid (Bonnet et al.,
1990). The boxes (20 per line) were randomly located in an
air-conditioned room (20°C, 90% relative humidity, 16 h:8 h L:D
photoperiod). The snails were fed ad libitum with a compound food
(Ets Arrive, France). Every week the boxes were cleaned, checked for dead
animals, and supplied with fresh food.
Growth pattern
The egg-layings were weighed and scored for eggs. Immediately after
hatching and every 3 weeks thereafter, the snails were weighed in family
groups and counted, and average snail mass per family box at the time was
calculated. Mass was measured to the nearest 0.01 g on an electronic balance.
Typically, H. aspersa snails cease growth after development of a
thickened lip at the shell aperture, the so-called peristome, and start
reproducing (Baker, 2001). Such
individuals were systematically removed from the boxes and weighed
individually to the nearest 0.001 g. This nonrandom removal of larger
individuals caused underestimation of average snail mass calculated in the
periods following such removals. To reduce the bias, average mass at a given
time was calculated from the mass of snails found in a box at that time and
from the mass of snails removed from the box before.
The shape of the growth trajectory of an average snail in a family box was
described by the logistic equation:
 | (2) |
),
where Mb (g) is body mass at age t (days),
M0 (g) is the hypothetical body size at age equal to 0,
MA (g) is the asymptotic size and k
(day–1) is the growth rate coefficient. The equation was
fitted to datasets on age and average snail mass in each box, using the least
squares method with the Simplex-quasi Newton procedure (Statistica 6.1,
StatSoft); average mass was weighted with the number of snails contributing to
its calculation. Data on egg mass were not used for curve fitting; snail age
was assumed to be zero days at the date of egg laying. Logistic growth is characterized by an inflection (at body size Mb=0.5MA), which demarcates ontogeny between two growth phases: growth accelerates with age before reaching the inflection; afterwards growth slows with age. Given this criterion, snails younger than the family-specific age at which the inflection was attained (hereafter AGEinflect) were defined as fast growing, and older snails as slow growing. The rate of growth at the inflection point was calculated from a derivative of Eqn 2, dMb/dt=kMb(MA–Mb)/MA, and used as a family-specific index of maximum growth rate, GRmax (g day–1).
Average egg number per clutch, average egg and hatchling mass, growth curve parameters M0, MA, k and maximum growth rate parameters GRmax and AGEinflect were compared with ANOVA (Statistica 6.1, StatSoft) in the control and size-selected lines. To normalize the distributions, the values of metric traits were transformed with decimal logarithms.
Consumption and growth
Weekly data on the amount of provided and uneaten food in each rearing box
were combined in three-week sets to match the time intervals over which snail
growth was monitored. Consumption over the intervals was calculated by
subtracting the dry mass of uneaten food from the dry mass of food provided to
the boxes; the dry mass of added food was estimated from a dry-to-wet mass
regression derived from preliminary data. Samples of food and refuse were
dried for 24 h at 103°C in a ventilated oven, and weighed dry to the
nearest 0.01 g. Consumption was converted to daily ration per snail
(C, g day–1). Snail growth was measured over 3-week
intervals as the gain of average snail mass in a box and converted to daily
growth rate (GR, g day–1). The average growth
efficiency of a snail in a box (GE, g g–1) was
expressed by the ratio GR:C.
General linear models (GLMs) were used to compare consumption rate C, growth rate GR and growth efficiency GE between the control and size-selected lines (Statistica 6.1, StatSoft). The models included three grouping factors: snail line (fixed), family box nested within line (random), and the 3-week interval over which the data were recorded (random); average snail body mass at the beginning of each interval was covariate. The relationship of GR and GE with the covariate was linear within time intervals, but became nonlinear for pooled data, indicating that the slope of the within-interval relation was changing across intervals. To account for this phenomenon, the GLMs for growth rate and growth efficiency included the body sizextime interval interaction. The analysis was performed only on data from the fast-growth period because the estimates of biomass increase and food consumption calculated from this period were least affected by snail removal and mortality. Prior to the analysis, the data were transformed with decimal logarithms to normalize the distributions and linearize the relationships between variables.
Rates of development and mortality
Snail survivorship in rearing boxes (families) was expressed by the median
life expectancy of snails at the beginning of life, calculated using the life
table method (Statistica 6.1, StatSoft). Snails alive at the end of the
experiment, snails removed from boxes after production of the peristome, and
individuals used for metabolic measurements were treated as censored
observations. The calculated median life expectancies were compared between
the control and size-selected lines with the Kruskal–Wallis
nonparametric test. A similar procedure was used to compare the age at which
control and size-selected snails produced the peristome. Dead individuals,
snails removed for metabolic measurements, and snails that did not develop the
peristome by the end of experiment were treated as censored observations.
Scaling of metabolism and shell mass
To measure metabolic rate, snails were sampled from the family boxes,
placed individually in plastic containers with holes in the lids, and shipped
via courier service in an isolation box to the Institute of
Environmental Sciences (IES), Jagiellonian University in Kraków,
Poland. To obtain a wide range of body sizes and to trace changes of metabolic
scaling across ontogeny, seven samples were taken, at approximately 3-week
intervals; the snails were between 11 and 33 days old at the first sampling.
One snail per family was chosen during the first three samplings, and two
snails per family in the following four samplings; only individuals weighing
close to the mean for the box were chosen. Snails were received at IES after
4–5 days; they were sprayed with dechlorinated tapwater and kept for 48
h in a 20°C chamber under a 16 h:8 h L:D photoperiod. Fifteen snails per
line, representing different families, were randomly chosen for measurement of
metabolic rate; occasionally two snails of the same family were used. Snails
were placed individually in 5x5 mm net curtain sacks to reduce their
activity, and enclosed in flasks (volume 50, 600, 1600 ml, depending on snail
size) connected to individual channels of a computer-controlled,
closed-circuit respirometer (Micro-Oxymax, Columbus Instrument, USA). An
Eppendorf tube with distilled water and a hole in the cap was placed in each
flask to maintain humidity. Flasks with control line and size-selected line
snails were placed alternately in 20°C chambers, kept illuminated during
the measurements to reduce snail activity. Oxygen intake was measured for 6 h,
and its consumption per hour was taken as a measure of metabolic rate
(hereafter O2;
µl h–1). After the measurements the snails were weighed on
an electronic balance to the nearest 0.001 g, then killed by freezing at
–20°C for 24 h; soft parts were removed and weighed. Whole mass
M and flesh mass MF were used as measures of
snail body size. Shell mass MS was calculated by
subtracting MF from M.
To compare metabolic size-scaling between fast- and slow-growth ontogenetic
phases, snails younger than the family-specific AGEinflect
were defined as fast-growing, and older snails as slow-growing. Slopes of
log10-log10 regressions of oxygen consumption
O2
versus whole snail mass M and flesh mass
MF (exponent b in Eqn
1) and slopes of log10–log10
regressions of shell mass MS versus flesh mass
MF were calculated for control line and size-selected line
snails, separately for their fast- and slow-growth phases. Confidence
intervals of regression slopes were used to compare metabolic exponents with
theoretical b values 1, 0.75 and 0.67. The GLM method was used to compare body
size-scaling of metabolic rate
O2 in control
versus size-selected lines, and in fast- versus slow-growth
phases. The model included a fixed factor of snail line or growth phase, whole
body mass M as covariate, and factor x covariate interaction. A
significant interaction was taken to indicate a difference in size-scaling
between snail lines or growth phases. To further investigate how metabolic
scaling was affected by shell scaling, a similarly structured GLM analysis was
performed on data on oxygen consumption and shell mass MS
in relation to snail flesh mass MF as covariate. All data
were log10-transformed prior to the analyses to normalize the
distributions and to linearize the relationships between variables.
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Consumption and growth
Food consumption rate C increased with snail size; it differed
between three-week measurement intervals
(Table 2,
Fig. 2A), but not between snail
lines or rearing boxes. GLM analysis showed that growth rate GR and
growth efficiency GE were higher in the selected than in the control
line (Table 2,
Fig. 2B,C); GR and
GE significantly differed between rearing boxes and between 3-week
intervals. Growth rate and growth efficiency were related to body size, but
this link was altered by the time interval, as indicated by the significant
body sizextime interval interaction for GR and GE.
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Rates of development and mortality
The Kruskal–Wallis test showed that median life expectancy tended to
be higher in the size-selected than in the control line (median values: 155.00
versus 130.55 days, H=3.55741, P=0.059). The median
expected age at which snails reached the adult stage (presence of peristome)
did not differ between the lines (Kruskal–Wallis test,
H=1.551941, P=0.21).
Scaling of metabolism and shell mass
Fig. 3 shows the
size-scaling of oxygen consumption and shell mass in the control and
size-selected snails, and changes of these scalings through ontogeny.
Table 3 reports the results of
GLM analysis of scaling of metabolism and shell mass, and
Table 4 gives estimates of the
size-scaling exponents for the two traits.
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The metabolic rate tended to increase isometrically with body size (b=1) in the fast-growth phase of the selected line (scaling with whole mass M and with flesh mass MF; Table 4, Fig. 3C); the scaling exponents were higher than 0.75. In the fast-growth phase of the control line, oxygen consumption scaled isometrically with size (and with a slope steeper than 0.75) when metabolism was regressed against whole mass M, and almost isometrically (with a slope steeper than 0.67 but not diverging from 0.75) when it was regressed against flesh mass MF (Table 4). In the slow-growth phase, the scaling was significantly lower than 1 but not diverging from 0.67 and 0.75 in both lines.
The size-scaling of metabolism was shallower in the slow- than in the fast-growth phase (Tables 3, 4, Fig. 3A,B). This tendency was observed in both lines, whether metabolism was scaled with whole body mass M or with flesh mass MF. GLM analysis showed that the shallowing of the slope of metabolic scaling through ontogeny was significant in the size-selected line when whole body mass was the size measure (Table 3: P=0.046 for interaction term) or marginally significant when the size measure was flesh mass (Table 3: P=0.087 for interaction term). In the control line, the ontogenetic shift in metabolic scaling was nonsignificant for both size measures (Table 3: P=0.24 and 0.47 for interaction terms).
In the fast-growth phase, the mass exponent for metabolism was larger for selected than for control snails (Tables 3, 4, Fig. 3C). The difference was significant when snail flesh mass was taken as the size measure (Table 3: P=0.045 for an interaction term); it was marginally significant when whole body mass was used (Table 3: P=0.087 for an interaction term). In the slow-growth phase, metabolism scaled at the same rate with size in both lines, no matter which measure of body size, M or MF, was considered (Table 3: P=0.95 and 0.92 for interaction terms).
Shell mass MS increased faster with flesh body mass MF in the slow- than in the fast-growth phase (Table 4); the change in scaling was marginally significant in the size-selected line (Table 3: P=0.065 for interaction term, Fig. 3E) and significant in the control (Table 3: P=0.008 for interaction term, Fig. 3D). In the fast-growth phase, shell mass tended to increase faster with body size in the selected than in the control line (Table 3: P=0.0826 for interaction term, Fig. 3F). Shell scaling did not differ between lines in the slow-growth phase (Table 3: P=0.80 for interaction term).
|
DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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).
Increased growth rates are achieved through either (1) an increase of
energy acquisition, (2) an increase of resource allocation to growth at the
expense of other energy-demanding processes (e.g. reproduction, maintenance),
or (3) lowering of the metabolic costs of growth
(Glazier, 1990;
Konarzewski, 1995;
Czarnoski and
Kozowski, 1998; Bayne,
1999; Konarzewski et al.,
2000). In general, accelerated growth is expected to increase
total metabolism as a result of elevated expenditures for biosynthesis and
tissue deposition (Jörgensen,
1988), but the interdependence of mechanisms 1–3 can lead to
different responses of total metabolism
(Konarzewski, 1995). For
example, fast-growing forms of the lake whitefish Coregonus
clupeaformis had lower food consumption and lower metabolism than
slow-growing dwarfs (Trudel et al.,
2001); artificial selection for increased body size in oysters
produced fast-growing individuals which consumed more food but used less
oxygen due to lower costs of growth (joules respired per joule of growth) and
decreased expenditure for maintenance
(Bayne, 1999). Interestingly,
MacLaury and Johnson (MacLaury and
Johnson, 1972) demonstrated that selection for increased oxygen
uptake can produce slow-growing organisms. In our study, fast- and
slow-growing lines of H. aspersa had similar food consumption.
Compared to the control line, the fast-growing selected line had higher growth
efficiency (Table 2,
Fig. 2C), and a lower (at
smaller body sizes) or equal (at larger sizes) metabolic rate
(Fig. 3C). These
characteristics point to the role of alteration of resource allocation (2) and
costliness of growth (3) in differentiating growth rates between the two
lines. Evolution of growth rates through resource allocation must involve
alterations in the energy provisioning of many functions which are
interconnected in complex ways, thus generating a wide array of different
tradeoffs (Metcalfe and Monaghan,
2001; Pigliucci and Preston,
2004; Czarnoski
et al., 2005). This study was not aimed at identifying such
tradeoffs, but our data allow us to look at whether size-selected snails
enhanced their growth at the expense of survivorship and shell production. We
found a concerted response of shell production and oxygen consumption to size
selection: in the phase of accelerating growth, the fast-growing selected line
had a lower metabolic rate and produced lighter shells than the slow-growing
control (Fig. 3C,F).
Interestingly, the difference in metabolic rate persisted as long as the
size-selected snails had lower shell mass than control snails: both lines
became similar with respect to metabolic rates after attainment of body size
MF equal to 0.498 g, which almost exactly coincided with
equalization of shell masses in the two lines at MF=0.490
g. Our results suggest that the increased expenditure for tissue growth in the
size-selected snails was at least in part covered at the expense of shell
production. Costs of shell production are often considered an important part
of the energy budget, and they are responsible for tradeoffs between shell
elongation and thickening (Palmer,
1992) (but see
Czarnoski et al.,
2006). Our analysis of snail mortality suggests that increased
growth rate was not realized at the expense of processes that determined
survivorship (e.g. maintenance). On the contrary, the mortality rate tended to
be lower in the fast-growing selected line than in the slow-growing control
line. We admit, however, that this finding might not be conclusive because,
for logistical reasons, we only measured juvenile mortality, in laboratory
conditions, under high food levels and in the absence of natural enemies.
Adverse effects of increased growth early in life are often not evident until
much later (Metcalfe and Monaghan,
2001; Monaghan and Haussmann,
2006); a fuller understanding of the costs of growth in snails
would require analyses of lifespan and mortality under unfavorable conditions
(infections, starvation, dehydration, hypothermia, estivation). For example,
the impairment of shell production in the selected snails suggests their
higher susceptibility to water loss through the shell, and less ability to
withstand predatory attacks.
Growth rate and metabolic scaling
Expenditure for growth processes can constitute a significant part of total
metabolism: in the toad Bufo bufo it reaches 60% of total metabolism
(Jörgensen, 1988). Given
that in fast-growing organisms the expenditure for biosynthesis and tissue
deposition increases, and that the growth rate changes proportionally to body
size, total metabolism is predicted to scale isometrically or almost
isometrically with body mass in fast growers, and negatively allometrically in
slow growers (Wieser, 1994;
Riisgård, 1998).
Reviving Bertalanffy's idea (von
Bertalanffy, 1957), Glazier
(Glazier, 2005) used this
concept to distinguish four major types of intraspecific metabolic scaling,
and he argued that much of the variability of metabolic scaling between
organisms (individuals, species and higher taxa) can be explained by the
effects of evolution of differential growth rates. For example, according to
Glazier (Glazier, 2006),
isometric metabolic scaling prevails in pelagic species because they evolved
fast growth, whereas negative allometry dominates in benthic species growing
relatively slowly. The results of our analysis of metabolic scaling in H.
aspersa snails are generally consistent with the concept of coupling
between growth rates and metabolic scaling. The size-dependence of metabolism
was isometric or almost isometric in the fast-growing, early ontogenetic
stages of snails (b=1.03 in the size-selected line, b=0.92 in the control
line), and it was negatively allometric in the slow-growing late stages
(b=0.74 in the size-selected line, b=0.75 in the control line)
(Table 4,
Fig. 3A,B); the ontogenetic
shallowing of metabolic scaling was statistically significant in the selected
but not in the control line (Table
3). The biphasic metabolic scaling detected in H. aspersa
resembles Glazier's Type III, and it has been reported in a wide range of
different organisms including copepods, marine invertebrates, insects, fish
and mammals (Brody, 1945;
Epp and Lewis, 1980;
Muthukrishan and Pandian,
1987; Post and Lee,
1996). For example, the metabolism of fast-growing Mytilus
edulis larvae and juveniles increased almost isometrically with size
(b=0.9) and negatively allometrically (b=0.7) in slow-growing adult
mussels (Riisgård,
1998). Our data also point to the importance of the growth rate in
explaining the between-organism variability of the mass exponent for
metabolism: scaling of metabolism was steeper in the fast-growing selected
line than in the slow-growing control
(Table 3,
Fig. 3C; note that this
difference persists only early in ontogeny).
Size-scaling of metabolism can be obscured by the allometry of
metabolically inert biomass such as reserve and skeletal material
(Glazier, 1991). For example,
in the amphipod Gammarus fossarum, whose proportion of metabolically
active protoplasm decreases while the proportion of metabolically inert chitin
increases with body size, metabolism scaled to the power of 0.65 with whole
body mass, but to the power of 0.95 in relation to protoplasm
(Simi and Brancelj,
2003). This demonstrates that an examination of coupling between
metabolic scaling and growth rates in molluscs should incorporate changes in
shell mass. Our data showed that the shell mass of H. aspersa snails
constituted up to 68% of whole body mass; its size-scaling differed between
the early and late phases of snail development
(Table 3,
Fig. 3D,E). Shell mass scaled
to the power of 0.79 (selected line) and 0.72 (control line) with flesh mass
in the fast-growth phase, and to the power of 0.98 and 1.02 in the slow-growth
phase (Table 4), which means
that the proportion of metabolically inert shell decreased with body size
during early growth and remained approximately constant throughout the
remainder of life. Although this suggests that shell size-scaling may have
affected our assessment of scaling of metabolism in the early (but not in the
late) ontogenetic stages, the exponents for H. aspersa metabolism
derived from the regressions of metabolism versus shell-free mass
resembled the estimates calculated from the regressions of metabolism
versus whole mass (Table
4). This result is similar to earlier findings
(Simi and Brancelj,
2003) that the metabolism of the amphipod G. fossarum
scaled at similar rates with whole and with non-chitinous mass, despite the
increase in the proportion of metabolically inert exoskeleton with body size.
Our comparison of the size-scaling of shell in selected versus
control lines of H. aspersa (Tables
3,
4,
Fig. 3F) revealed that scaling
in the two lines was similar when analyzed in late ontogenetic stages, but
early in ontogeny the proportion of shell mass increased faster with body size
in the selected than in the control line. Note that this difference cannot
account for our finding that the metabolism of selected snails scaled faster
with whole body mass than in the control. Just the opposite: such a difference
should sharpen the between-line difference in metabolic scaling when data on
shell-free mass are considered instead of data on whole mass, and we found
such a tendency (Table 3).
Overall, removing the effects of metabolically inert shell mass did not change
the general picture of size-scaling of metabolism derived from the analysis
based on the whole mass of snails (Table
3). This strengthens the primary evidence on the role of growth
rates in explaining variability of metabolic scaling in H. aspersa
snails.
Linking metabolic scaling, growth and cell size – future prospects
Altogether, our data showed that the size-scaling of metabolism in H.
aspersa snails was isometric or nearly isometric, and significantly
steeper than 2/3 and 3/4 size-scalings early in ontogeny, and became shallower
and not different from the 2/3 and 3/4 scaling modes later in life
(Table 4). These findings
complement emerging evidence on the variability of the mass exponent for
metabolism in nature (e.g.
Kozowski et al., 2003;
Glazier, 2005;
Glazier, 2006). The new
research challenges the traditional conviction that 2/3 or 3/4 size-scalings
are the norm, a view still dominating currently developed theories in ecology
(Brown et al., 2004), and
raises the question of the mechanisms explaining this diversity
(Glazier, 2005;
Chown et al., 2007). Our
results favor the view that some part of this variability can be linked to
variable growth rates, but we stress that this concept in its original form
(sensu Wieser, 1994;
Riisgård, 1998)
overlooks the potential metabolic consequences of cellular processes
associated with growth rate changes. Most organisms increase body size mainly
through cell proliferation (hyperplasia) during early postembryonic
development and thus with relatively little change in average cell size, but
later in life mainly by cell growth and/or hypertrophy
(Falconer et al., 1978;
Atchley et al., 2000;
Glazier, 2005). Given that
larger cells require less energy per protoplasm volume than smaller cells for
maintenance of ion gradients across cell membranes
(Davison, 1955;
Kozowski et al.,
2003), cellular changes through ontogeny alone should lead to
nearly isometric scaling of metabolism early in life and to negative allometry
of metabolism later in life, a phenomenon already postulated
(Kayser and Heusner, 1964) and
quoted (Medrano and Gall,
1976b). Thus, it is reasonable to suggest that biphasic
size-scaling of metabolism is produced by the joined effects of ontogenetic
shifts in energy expenditure for growth and changes in the relative roles of
hyperplasia and hypertrophy in ontogenetic growth. Changes in the size and
number of cells are also known to underlie responses to selection for growth
traits (Medrano and Gall,
1976a; Falconer et al.,
1978; Stevenson et al.,
1995; Atchley et al.,
1997; Atchley et al.,
2000; Calboli et al.,
2003). Partridge et al.
(Partridge et al., 1999), for
example, demonstrated that lab evolution of larger Drosophila
melanogaster proceeded mainly through increasing cell number, and an
evolutionary decrease in size was achieved mostly by reduction of cell size.
Such cellular changes should contribute to coupling between growth rates and
metabolic scaling on evolutionary scales, but this issue remains largely
unexplored. In line with that view, Glazier
(Glazier, 2005) suggested that
dissimilar cellular mechanisms of ontogenetic growth could be a proximate
explanation of shallower metabolic scaling in nematodes (0.677) than in squid
species (1): hypertrophy characterizes nematodes and hyperplasia prevails
in squids. Similarly, Chown et al. (Chown
et al., 2007) demonstrated that in ant species where changes in
cell size are a main proximate mechanism explaining intraspecific variation in
body size, metabolic rate scales isometrically with body size, whereas in the
species where cell size does not contribute to body size variation, the
scaling becomes negatively allometric. On a higher taxonomic level,
Kozowski et al. (Kozowski
et al., 2003) showed that differences in interspecific scaling of
the basal metabolic rate between orders within mammals and birds are linked to
differential size-scaling of genomes. Thus, on a macroevolutionary scale, cell
size appears to change, at least in part, through alterations in the amount of
DNA packed in nuclei, and the cellular outcome of this evolution can influence
interspecific size-scaling of metabolism. We stress that future studies should
reconcile the cellular and physiological (1–3) mechanisms associated
with growth rate evolution, and should investigate their role in the origin of
metabolic scaling variability on different levels of biological organization.
Successful integration of these phenomena promises evolutionary explanations
of different large-scale phenomena such as Bergmann's rule in ectotherms or
patterns in interspecific body size distributions and life histories
(Kozowski and Weiner,
1997; Kindlemann et al.,
1999; Kozowski and
Gawelczyk, 2002; Angilletta and
Dunham, 2003; Kozowski
et al., 2003; Kozowski
et al., 2004).
LIST OF SYMBOLS AND ABBREVIATIONS
O2
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Acknowledgments |
|---|
/757/06). We thank A. Chyliska and U. Pokorska for assistance in collecting data, and J. Gautier, J. L. Widiez and M. Ros for help in rearing the animals. M. Jacobs helped edit the manuscript.
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References |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
|---|
Angilletta, M. J., Jr and Dunham, A. E. (2003). The temperature-size rule in ectotherms: simple evolutionary explanations may not be general. Am. Nat. 162,332 -342.[CrossRef][Medline]
Atchley, W. R., Xu, S. and Cowley, D. E. (1997). Altering developmental trajectories in mice by restricted index selection. Genetics 146,629 -640.[Abstract]
Atchley, W. R., Wei, R. and Crenshaw, P.
(2000). Cellular consequences in the brain and liver of
age-specific selection for rate of development in mice.
Genetics 155,1347
-1357.
Baker, G. M. (2001). The Biology of Terrestrial Molluscs. New York: CABI Publishing.
Bayne, B. L. (1999). Physiological components of growth differences between individual oysters (Crassostrea gigas) and a comparison with Saccostrea commercialis. Physiol. Biochem. Zool. 72,705 -713.[CrossRef][Medline]
Bonnet, J. C., Aupinel, P. and Vrillon, J. L. (1990). L'escargot Helix aspersa: biologie, élevage. Paris: INRA.
Brody, S. (1945). Bioenergetics and Growth. New York: Reinhold.
Brown, J. H., Enquist, B. J. and West, G. B. (1997). Allometric scaling laws in biology. Science 278,373 .
Brown, J. H., Gillooly, J. F., Allen, A. P., Van Savage, M. and West, G. B. (2004). Toward a metabolic theory of ecology. Ecology 85,1171 -1789.[CrossRef]
Calboli, F. C. F., Gilchrist, G. W. and Partridge, L. (2003). Different cell size and cell number contribution in two newly established and one ancient body size cline of Drosophila subobscura. Evolution 57,566 -573.[CrossRef][Medline]
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. 2,282 -290.
Czarnoski, M. and Kozowski, J.
(1998). Do Bertalanffy's growth curves result from optimal
resource allocation? Ecol. Lett.
1, 5-7.[CrossRef]
Czarnoski, M., Kozowski, J.,
Staczykowska, A. and Lewandowski, K. (2003). Optimal resource
allocation explains growth curve diversity in zebra mussels. Evol.
Ecol. Res. 5,571
-587.
Czarnoski, M., Kozowski, J., Lewandowski,
K., Miko ajczyk, M., Müller, T. and Staczykowska, A.
(2005). Optimal resource allocation explains changes of the zebra
mussel growth pattern through time. Evol. Ecol. Res.
7, 821-835.
Czarnoski, M., Kozowski, J., Kubajak, P.,
Lewandowski, K., Müller, T., Staczykowska, A. and Surówka, K.
(2006). Cross-habitat differences in crush resistance and growth
pattern of zebra mussels (Dreissena polymorpha): effects of calcium
availability and predator pressure. Arch. Hydrobiol.
265,191
-208.
Davison, J. (1955). Body weight, cell surface
and metabolic rate in anuran Amphibia. Biol. Bull.
109,407
-419.
Dupont-Nivet, M., Guiller, A. and Bonnet, J. C. (1997). Genetic and environmental variability of adult size in some stocks of the edible snail, Helix aspersa Müller. J. Zool. 241,757 -765.
Dupont-Nivet, M., Mallard, J., Bonnet, J. C. and Blanc, J. M. (2000). Direct and correlated responses to individual selection for large adult weight in the edible snail Helix aspersa Müller. J. Exp. Zool. 287, 80-85.[CrossRef][Medline]
Epp, R. W. and Lewis, W. M. (1980). The nature and ecological significance of metabolic changes during the life history of copepods. Ecology 61,259 -264.[CrossRef]
Falconer, D. S., Gauld, I. K. and Roberts, R. C. (1978). Cell numbers and cell sizes in organs of mice selected for large and small body size. Genet. Res. Camb. 31,287 -301.[Medline]
Furness, R. W. (2003). It's in the genes. Nature 425,779 -780.[CrossRef][Medline]
Glazier, D. S. (1990). Constraints on the offspring production efficiency of Peromyscus and other rodents. Funct. Ecol. 4,223 -231.[CrossRef]
Glazier, D. S. (1991). Separating the respiration rates of embryos and brooding females of Daphnia magna: implications for the cost of brooding and the allometry of metabolic rate. Limnol. Oceanogr. 36,354 -362.
Glazier, D. S. (2005). Beyond the `3/4-power law': variation in the intra-and interspecific scaling of metabolic rate in animals. Biol. Rev. 80,611 -662.[Medline]
Glazier, D. S. (2006). The 3/4-power law is not universal: evolution of isometric, ontogenetic metabolic scaling in pelagic animals. BioScience 56,325 -332.[CrossRef]
Heusner, A. A. (1982). Energy metabolism and body size. I. Is the 0.75 mass exponent of Kleiber a statistical artifact? Respir. Physiol. 48,1 -12.[CrossRef][Medline]
Jörgensen, C. B. (1988). Metabolic costs
of growth and maintenance in the toad, Bufo bufo. J. Exp.
Biol. 138,319
-331.
Kayser, Ch. and Heusner, A. (1964). Étude comparative du métabolisme énergetique dans la série animale. J. Physiol. Paris 56,489 -524.[Medline]
Kindlemann, P., Dixon, A. F. G. and Dostálková, I. (1999). Does body size optimization result in skewed body size distribution on a logarithmic scale? Am. Nat. 153,445 -447.[CrossRef]
Konarzewski, M. (1995). Allocation of energy to growth and respiration in avian postembryonic development. Ecology 76,8 -19.[CrossRef]
Konarzewski, M., Gavin, A., McDevitt, R. and Wallis, I. R. (2000). Metabolic and organ mass responses to selection for high growth rates in the domestic chicken (Gallus domesticus). Physiol. Biochem. Zool. 73,237 -248.[CrossRef][Medline]
Kooijman, S. A. L. M. (2000). Dynamic Energy and Mass Budgets in Biological Systems. Cambridge: Cambridge University Press.
Kozowski, J. and Gawelczyk, A. (2002).
Why are species' body size distributions usually skewed to the right?
Funct. Ecol. 16,419
-432.[CrossRef]
Kozowski, J. and Teriokhin, A. T.
(1999). Allocation of energy between growth and reproduction: the
Pontryagin Maximum Principle solution for the case of age- and
season-dependent mortality. Evol. Ecol. Res.
1, 423-441.
Kozowski, J. and Weiner, J. (1997).
Interspecific allometries are by-products of body size optimization.
Am. Nat. 149,352
-380.[CrossRef]
Kozowski, J., Konarzewski, M. and Gawe czyk, A.
(2003). Cell size as a link between noncoding DNA and metabolic
rate scaling. Proc. Natl. Acad. Sci. USA
100,14080
-14085.
Kozowski, J., Czarnoski, M. and Dako,
M. (2004). Can optimal allocation models explain why
ectotherms grow larger in cold? Integr. Comp. Biol.
44,480
-493.
Lochmiller, R. L. and Deerenberg, C. (2000). Trade-offs in evolutionary immunology: just what is the cost of immunity? Oikos 88,87 -98.[CrossRef]
MacLaury, D. W. and Johnson, T. H. (1972). Selection for low oxygen consumption in chickens. Poult. Sci. 51,591 -597.[Medline]
Martin, L. B., II, Scheuerlein, A. and Wikelski, M. (2003). Immune activity elevates energy expenditure of house sparrows: a link between direct and indirect costs? Proc. R. Soc. Lond. B Biol. Sci. 270,153 -158.[Medline]
McMahon, T. A. (1973). Size and shape in
biology. Science 179,1201
-1204.
Medrano, J. F. and Gall, G. A. E. (1976a). Growth rate, body composition, cellular growth and enzyme activities in lines of Tribolium castaneum selected for 21-day pupa weight. Genetics 83,376 -391.
Medrano, J. F. and Gall, G. A. E. (1976b). Food
consumption, feed efficiency, metabolic rate and utilization of glucose in
lines of Tribolium castaneum selected for 21-day pupa weight.
Genetics 83,393
-407.
Metcalfe, N. B. and Monaghan, P. (2001). Compensation for a bad start: grow now, pay later? Trends Ecol. Evol. 16,254 -260.[CrossRef][Medline]
Monaghan, P. and Haussmann, M. F. (2006). Do telomere dynamics link lifestyle and lifespan? Trends Ecol. Evol. 21,47 -53.[CrossRef][Medline]
Muthukrishan, J. and Pandian, T. J. (1987). Insecta. In Animal Energetics. Vol.2 (ed. T. J. Pandian and F. J. Vernberg), pp.373 -511. San Diego: Academic Press.
Palmer, A. R. (1992). Calcification in marine
molluscs: how costly is it? Proc. Natl. Acad. Sci. USA
89,1379
-1382.
Partridge, L., Langelan, R., Fowler, K., Zwaan, B. and French, V. (1999). Correlated responses to selection on body size in Drosophila melanogaster. Genet. Res. Camb. 74, 43-54.[CrossRef][Medline]
Peters, R. H. (1983). The Ecological Implications of Body Size. New York: Cambridge University Press.
Pigliucci, M. and Preston, K. (2004). Phenotypic Integration Studying the Ecology and Evolution of Complex Phenotypes. Oxford: Oxford University Press.
Plorin, G. and Gilbertson, D. E. (1984). Equation for describing growth of the schistosome host snail Biomphalaria glabrata. Physiol. Zool. 46,252 -275.
Post, J. R. and Lee, J. A. (1996). Metabolic ontogeny of teleost fishes. Can. J. Fish. Aquat. Sci. 53,910 -923.[CrossRef]
Radwan, J., Chadziska, M., Cicho, M., Mills, S. C., Matula, B., Sadowska, E. T., Baliga, K., Stanisz, A., Lopuch, S. and Koteja, P. (2006). Metabolic costs of sexual advertisement in the bank vole (Clethrionomys glareolus). Evol. Ecol. Res. 8, 859-869.
Riisgård, H. U. (1998). No foundation of a `3/4 power scaling law' for respiration in biology. Ecol. Lett. 1,71 -73.[CrossRef]
Rubner, M. (1883). Über den Einfluss der Körpergrösse auf Stoffund Kraftwechsel. Z. Biol. Munich 19,535 -562.
Schmidt-Nielsen, K. (1984). Scaling: Why is Animal Size so Important? New York: Cambridge University Press.
Sibly, R. M. and Calow, P. (1986). Physiological Ecology of Animals. An Evolutionary Approach. Oxford: Blackwell Scientific Publications.
Simi, T. and Brancelj, A.
(2003). Estimation of the proportion of metabolically active mass
in the amphipod Gammarus fossarum. Freshw. Biol.
48,1093
-1099.[CrossRef]
Sinervo, B. and Huey, R. B. (1990). Allometric
engineering: an experimental test of the causes of interpopulational
differences in performance. Science
248,1106
-1109.
Stevenson, R. D., Hill, M. F. and Bryant, P. J. (1995). Organ and cell allometry in Hawaiian Drosophila: how to make a big fly. Proc. R. Soc. Lond. B Biol. Sci. 259,105 -110.[Medline]
Trudel, M., Tremblay, A., Schetagne, R. and Rasmussen, J. B. (2001). Why are dwarf fish so small? An energetic analysis of polymorphism in lake whitefish (Coregonus clupeaformis). Can. J. Fish. Aquat. Sci. 58,394 -405.[CrossRef]
von Bertalanffy, L. (1951). Metabolic types and growth types. Am. Nat. 85,111 -117.[CrossRef]
von Bertalanffy, L. (1957). Quantitative laws in metabolism and growth. Q. Rev. Biol. 32,217 -231.[CrossRef][Medline]
West, G. B., Brown, J. H. and Enquist, B. J.
(1997). A general model for the origin of allometric scaling laws
in biology. Science 276,122
-126.
Wieser, W. (1994). Cost of growth in cells and organisms: general rules and comparative aspects. Biol. Rev. Camb. Philos. Soc. 69,1 -34.[Medline]
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