University of Groningen
The cost of living
Schmitz, Cordula
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Schmitz, C. (2010). The cost of living: Temperature compensation of the metabolic rate in plants
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2
Temperature effects
on the metabolism of ectotherms:
Does Darwin break the Arrhenius law?
Cordula Schmitz
Barbara V. Feldmeyer
Bernd P. Freymann
Han Olff
Franjo J. Weissing
Ido Pen
J. Theo M. Elzenga
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Abstract
The Metabolic Theory of Ecology, which presumes that the basal metabolism of organisms can be derived from first principles, based on physical and chemical processes, is
a controversial theory. The master equation that describes the biomass and temperature-dependence of the basal metabolic rate (B) is B = b0 * M 3/4 * e–E/kT, where, b0 is
the species-specific normalization constant, the term M 3/4 describes the scaling on
biomass (M) and the Arrhenius-term (e–E/kT ) is a formalization of the thermodynamic
effect of temperature on the metabolic reaction rate of key-enzymes.
The respiratory temperature-dependence is considered to be similar for all species.
However, the enzymatic network of organisms is more complex than a single enzymatic reaction and by acclimation and adaptation organisms can adjust their metabolism to match their energy requirement under different conditions. Thus the response
to temperature should be species-specific and we expect, contrary to the predictions of
the Metabolic Theory of Ecology, differences in the enzymatic network of organisms in
which thermal acclimation and adaptation has taken place.
Here, we present a framework, based on an analysis of the literature and on experimental data, outlining the possible impact of exposure to a different temperature for
short-term, intermediate (within a lifetime) and long-term (covering many generations). The short-term temperature response can be described by the thermodynamics
of the species-specific enzymatic network. By acclimation and adaptation to high
temperatures the respiration is lower than dictated by thermodynamics only. The
apparent adjustments of the enzymatic network when exposed to a different temperature can mathematically be described with a species-specific normalization constant,
counteracting the direct thermodynamic effect as given by the Arrhenius equation.
.
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Chapter 2
Introduction
The temperature-dependence of the metabolism of organisms is determined by the
properties of their enzymatic metabolic network. A commonly used measure of metabolic rate is respiration, reflecting the energy requirement of metabolic processes. The
total respiration of an organism is the sum of basal metabolism, growth metabolism
and activity metabolism (Cannell & Thornley 2000). Thus differences in the maintenance cost, growth rate or activity level can result in differences in metabolic rate. The
respiration required for growth and activity is highly variable, dependent on developmental stage and behaviour. Juveniles have a higher growth rate than adults and
therefore a higher growth metabolism, resulting in higher respiration. Differences in
behavioural patterns within and between species, like higher reproductive investment
or activity, also result in different energy requirements (Clarke & Fraser 2004). In
contrast, basal metabolism is relatively stable. Species have similar tissue properties
and similar energy supplying enzymatic reactions and, therefore, relatively similar
maintenance costs.
However, temperature affects the turnover rate of enzymatic reactions and thus
basal metabolic rate. For many enzymatic reactions the temperature-dependence of
the turnover rate (U) can be described by the Arrhenius relation:
U = U0 * e–E/kT
[1]
Where U0 is the temperature-normalized turnover rate of an enzyme for the given
substrate and enzyme concentrations and the term e–E/kT is a description of the effect
of temperature. Here T is the absolute temperature (in °K), E is the activation energy
of the enzyme (in eV) and k is the Boltzmann constant, (8.62 * 10-5 eV/K). In an
Arrhenius plot, graphing the logarithmical transformed turnover rate against the
inverse of temperature multiplied by the Boltzmann constant, the effect of temperature is shown as a linear relation, where the slope represents the activation energy E
and the intercept, the temperature-normalized turnover rate U0.
The Arrhenius relation can only be applied to a process that involves a chain of
enzymatic reactions if: 1. the overall turnover rate is determined by a single ratelimiting step and 2. this same reaction stays the limiting over the whole temperature
range under consideration. Exposing a chain of enzymatic reactions to different
temperatures may, however cause the rate-limiting step to shift from one enzyme reaction to another. Whenever such a shift in the limiting step of a chain of enzymatic
reactions occurs, the Arrhenius relationship is expected to “jump” to another Arrhenius
relationship, one with a lower activation energy and reflecting the difference in activation energy between the enzymes involved. The enzymatic reactions involved in respiration are more complex than a simple linear chain of enzymatic reactions. The
metabolism of an organism is governed by a network of enzymatic reactions where the
rate limitation is distributed over the entire enzymatic network (Bruggeman &
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Darwin & Arrhenius
Westerhoff 2007). It is therefore unlikely that the turnover rate of the entire enzymatic
network can be described by the Arrhenius relationship.
Another complication is introduced by phenotypic and genotypic variations in
organisms with different thermal life histories. The feedback control presented by
these variations can result in reaction rates that are relatively independent of temperature (Hochachka & Somero 2002). Acclimation and adaptation will adjust the basal
metabolic rate to the prevailing temperature (Clarke 1993; Clarke & Johnston 1999;
Amthor 2000; Atkin & Tjoelker 2003; Gifford 2003; Clarke 2004; Clarke 2007;
Ghalambor et al. 2007), possibly by changes in the enzyme and substrate concentration or by the expression of enzyme isoforms with different temperature-dependence
(Bullock 1955; Chatterton et al. 1970; Atkin et al. 2003). As a consequence of the
differences in their enzymatic networks, organisms from warmer environments do not
necessarily have a higher basal metabolic rate.
Phenotypic plasticity can lead to acclimation that occurs during the lifetime of an
organism. The phenotypic plasticity of a species determines how fast and to what
extent metabolism can adjust to a different temperature (Chatterton et al. 1970). Full
acclimation to the prevailing temperature can take as short as a few days, but also as
long as months (Stamou et al. 1995; Atkin et al. 2000a; Chambell et al. 2007).
Extending the duration of exposure to different temperatures to multiple generations
can lead to genetic changes that compensate for the thermodynamic effect of temperature on the basal metabolic rate (Feder 1976; Somero 1978).
Adjusting basal metabolism by acclimation and adaptation does benefit an
organism, as it can contribute to the optimization of the balance between energy
supply and demand, and will therefore typically be related to fitness (Parsons 2005). A
low basal metabolic rate is a selective advantage under conditions of oxygen limitation, e.g. in the deep sea (Atkin & Day 1990; Childress 1995). In contrast, a high basal
metabolic rate is advantageous when a stronger immune defence is required (Colditz
2002; Klasing 2004). In addition, modifications of the basal metabolic rate can be
instrumental in maintaining homeostasis in primary and secondary metabolism (Atkin
et al. 2006a) and in providing reduction equivalents used for several processes such as
amino acid synthesis (Amthor 2000). By acclimation and adaptation organisms can
prevent temperature-induced shortage of energy supply at low temperatures or excessive energy use in warm climates.
By distinguishing between short, intermediate and long-term effect of temperature
exposure on basal respiration the effects of acclimation and adaptation of the enzymatic network of organisms can be separated from the thermodynamic effect of
temperature on enzymatic reactions.
Multiple studies on the temperature-dependence of respiration of different organisms show that the short-term respiratory temperature-dependence of an ectotherm
organism within the viable range (0–40°C) is exponential and can therefore described
by the Arrhenius relation (Aleksiuk 1971; Brechignac & Furbank 1987; Hirche 1987;
Somme et al. 1989; Clarke & Johnston 1999; van Iersel & Lindstrom 1999)
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Chapter 2
The basal metabolic rate is further determined by the body mass (see Chapter 1).
The combined effect of temperature and body mass is formalized in the following
equation put forward by Brown and co-workers (Gillooly et al. 2001; Brown et al.
2004), which has become known as the Metabolic Theory of Ecology (MTE):
B = b0 * M 3/4 * e–E/kT
[2]
where, b0 is a species-specific normalization constant, the term M 3/4 describes the 3/4 power law-dependence on biomass presumed from the fractal-like distribution
network of organisms (West et al. 1997) and the Arrhenius-term (e–E/kT ). Although the
exponent of the body mass term (and the underlying principle of the scaling relation)
is still under debate, the effect of mass has to be taken into consideration in any
analysis comparing the metabolism between organisms.
Apart from proposing that the MTE equation has universal validity, the MTE also
states that all organisms share the same metabolic key-enzymes with identical characteristics. The activation energy is therefore invariant between species and environmental conditions, and has a value of 0.62 eV (Gillooly et al. 2001). Explicitly, the
MTE precludes acclimation and adaptation to act on enzyme reaction chains and
networks in a way that possibly counteracts (or at least modifies) the thermodynamic
effect of temperature. In the present study this aspect of the MTE was tested by quantifying the short-term, intermediate and long-term effect of temperature and determining whether the possible effects of acclimation (when intermediate exposure times
are applied) or adaptation (comparing species from different temperature zones) are
indeed incapable of modifying the parameters of the Arrhenius relation.
Firstly, a theoretical framework is described to accommodate the possible effect on
the metabolic rate of temperature with short-term, intermediate (acclimated) and
long-term (adaptive) exposure. Secondly, data from a literature survey on the effects
under the three conditions is presented. These data clearly show differences in shortterm, acclimated and adapted temperature-dependence of the basal metabolic rate of
whole organisms. Thirdly, the theoretical framework is applied to the data obtained.
The theoretical framework
For the theoretical framework three assumptions were made:
Assumption 1: The short-term temperature response can predicted from the thermodynamic effect of temperature on the enzymatic network.
Assumption 2: Acclimation can lead to the use of different isoforms of enzymes and to
changes in the enzymatic environment (e.g. membrane fluidity) that lead to different
activation energies of the respiratory chain.
Assumption 3: Different species use different enzyme isoforms resulting in highly variable enzymatic networks, including different key (rate limiting) enzymes. As a consequence different species can exhibit different apparent activation energies of their basal
metabolism. Thus the activation energy should be species-specific: E = E(species).
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ln basal metabolic rate
Darwin & Arrhenius
organism
grown/evolved
at:
T3 cold
T2 mediate
T1 warm
1/T1
1/T2
1/T3
1/T
Figure 2.1 The Arrhenius plot shows the linear dependence of the logarithmical transformed
basal metabolic rate to the inverse of temperature for three organisms grown/evolved at
different temperatures. The differences in the temperature-normalized basal metabolism, given
by the intercept of the short-term temperature response, illustrate the considered changes in the
metabolic network. Organisms acclimated/adapted to high temperatures have a decreased
temperature-normalized respiration. Therefore the acclimated/adapted temperature response of
the basal metabolic rate is lower compared to the short-term temperature response.
In contrast to the immediate thermodynamic effect, the acclimated and adapted
temperature-dependence is the result of modifications in the enzymatic network at
different temperatures. Therefore the slopes of the acclimated and adapted temperature-dependence do not reflect the average activation energies of the enzymatic
network anymore. Instead, the slope of the acclimated and adapted response should
be regarded as an apparent activation energy (EACC & EAD). Differences in the enzymatic network over time should result in changes of the temperature-normalized
respiration of the short-term temperature-dependence, which also partly explains the
lower acclimated and adapted temperature response (see Figure 2.1).
Acclimation and adaptation result in a basal metabolic rate which is relatively
independent of temperature, and their effect could be described as a variable, which
counteract the ‘Arrhenius response’ of the basal metabolic rate to temperature. A
formalization of this effect of the adaptation temperature (TAD) on basal metabolism is
an ‘inverse Arrhenius-term’: eG/kT . Where, the coefficient G, reflects the strength of the
genetic adaptation of a species to the climatic conditions in its current habitat.
Assuming that the adaptive effect does not ‘overcompensate’ the thermodynamic effect
of temperature, the parameter describing the changes in the enzymatic network is positive and lower than, or equal to, the effect of the average activation energy (0 ≤ G ≤ E).
Likewise, the effect of acclimation can also be described by an ‘inverse Arrheniusterm’. For acclimation the plasticity (P) of the organism determines the strength of the
response. The plasticity describes the relative response in relation to adaptation
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Chapter 2
temperature and therefore should be dependent on the difference between the acclimation (TACC) and adaptation temperature (TAD): e P/k * (1/TACC – 1/TAD) .
The plastic response of an organism is time-dependent and will gradually build up
until the fully acclimated state has been reached (Stamou et al. 1995; Bouchard &
Guderley 2003). Although the genetic properties that determine the reaction norm of
a plastic response could depend on the adaptation temperature, there is little support
in literature that the genetic variability in plasticity indeed depends on the adaptation
temperature (Knies et al. 2006; Ghalambor et al. 2007). Therefore we preliminary
consider that the plasticity is time variant and species-specific: P = P (time, species)
Correcting the basal metabolism of organisms for the effect of acclimation and
adaptation temperature in addition to the predictions given by the MTE results in:
B = β 0 * M 3/4 * eG/kTAD * e P(time, species)/k * (1/TACC – 1/TAD) * e –E (species)/kT
[3]
Where, β 0 is the revised species-specific normalization constant b0, independent of
acclimation and adaptation temperature.
This equation describes that, the basal metabolism of organisms for a short period
exposed to different temperatures is exponentially dependent on inverse temperature.
For the short-term temperature response the slope of the logarithmically transformed
respiration to the inverse temperature is given by the average activation energies of
the limiting metabolic key-enzymes of the species. The acclimation- and adaptationinduced changes in the enzymatic network result in differences in the species-specific
normalization constant. With increasing acclimation or adaptation temperature the
normalization constant is decreased to counterbalance the thermodynamically determined increase in respiration at high temperatures. Thus the acclimated and adapted
respiratory temperature-dependence of organisms is lower than the short-term
temperature response.
Material and methods
Data collection
We compiled data of whole-organism respiratory rates, which were measured at
different temperatures, from a total of 40 publications. The collection, including both
laboratory and field studies, comprises a broad range of taxa, from plants to large
ectotherm animals (Appendix 1). Importantly, we exclusively included data on basal
or resting metabolic rates. Measurements had thus to be taken either on adult animals
in a resting state or, in case of plants, on whole shoots or full-grown leaves at darkness. We did not include data from endotherms since, in order to maintain a constant
body temperature, these animals increase metabolism when the environment cools
down and thus do not respond to external temperature the way ectotherms do.
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Darwin & Arrhenius
Categorization of data
Non-acclimation: In order to quantify the short-term temperature responses, organisms must have been exposed to the measurement temperature for a relatively short
time period, so that acclimation to the measurement conditions could not occur. By
the very nature of these measurements, the short-term temperature response always
relates to intraspecific data.
Acclimation: In order to guarantee acclimation individuals had to be exposed to
the respective temperature for a certain period of time prior to the measurement. The
time criterion for acclimation is species-specific and therefore difficult to generalize.
That, organisms were acclimated either had to be stated explicitly by the authors, or
was assumed to be the case when organisms had been acclimated for at least 48
hours. To exclude interference of adaptive effects, the acclimated response was determined on intraspecific data.
Adaptation: Individuals had to be acclimated to a specific measurement temperature, namely the one that closely match the mean growing season temperature in the
region of origin. Since many plants or ectotherm animals are either annual species or,
in case of a perennial growth strategy, shut down metabolism during winter, we expect
basal metabolic rate to be adapted to mean growing season temperature rather than
mean annual temperature (see also Kerkhoff et al. 2005). If the growing season or
growing season temperature was not reported by the authors we used the sampling
site specification in the articles in order to infer mean growing season temperature
from the internet (www.klimadiagramme.de). In that case growing season for terrestrial species was assumed to cover those months with a minimum mean temperature
of 4°C, while the marine polar zooplankton in deep waters is expected to grow at the
prevailing, relatively constant, temperature. The adapted temperature response can be
only determined on interspecific data.
Standardization of data
All data were statistically analysed as oxygen consumption rate and expressed in
Watts. Respiration given as carbon dioxide production had to be converted by a factor,
which depends on the ratio of the amount of CO2 produced over amount of O2
consumed. This respiratory quotient (RQ) was considered to be 1 in carbohydratemetabolizing plants (Tcherkez et al. 2003), and to equal 0.8 in fasted animals metabolizing an mixture of carbohydrates, fats, and proteins (Richardson 1929; Butler et al.
2004). Original data given in mass-related units such as grams, litres or moles were
first converted to millilitres and subsequently to the energy unit, assuming that 1 ml of
respired oxygen yields 20.9 Watt for plants and 20.1 Watt for animals (Richardson
1929; Schmidt-Nielsen 1997).
Since whole organism basal metabolic rate increases with body mass data were
divided by body mass3/4. If articles provided no information about body size, approximate sizes were gathered from other publications. Mass data provided as gram dry
weight were converted to gram fresh weight, assuming the water content to be 82% in
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Chapter 2
plants (Shipley & Vu 2002), and 75% in prawns (Ricciardi & Bourget 1998). In order
to adjust measurements on a carbon base, carbon content in plants is assumed to be
8% (Poorter & de Jong 1999). Generally, plant respiration data, which were typically
given as respiration per leaf mass, were first extrapolated to whole plant respiration
according to (Poorter & Remkes 1990; Tjoelker et al. 1999; Loveys et al. 2003).
Furthermore we corrected whole plant respiration for allocation to growth metabolism
according to (Cannell & Thornley 2000).
To be able to quantify the temperature effect separately for each species in the
categories non-acclimation or acclimation, each species had to be represented by a
minimum of three data points. Obviously in the subset of data on adaptation each
species is of course represented by only one data point.
Before statistical analysis the data were ln-transformed. As follows from the Arrhenius
relation the ln-transformed data are predicted to exhibit a linear relationship with the
inverse of temperature (1/kT).
In studies, where the acclimation temperature of organisms varies, the short-term
temperature response was determined on the data at the acclimation temperature
closest to the mean growing season temperature in the region of origin. In case the
mean growing season temperature was not available, the short-term temperature
response is expressed as the average of the different available acclimation temperatures.
The respiration at 20°C of the short-term temperature response at different acclimation and adaptation temperatures is used to standardize the respiration. This respiration at 20°C is calculated from the regression of the linear transformed nonacclimated data. Thus the temperature-standardized respiration is given by logarithmical transformed respiration at 20°C (ln R20).
To calculate the plastic/acclimated response of organisms the temperature-standardized respirations are calculated from the individual short-term temperature
responses at each acclimation temperature. Only data are included where the
organism was acclimated to at least three different temperatures.
To detect the influence of thermal adaptation on the temperature-standardized
respiration only those data were used where, the short-term temperature response was
measured on organisms acclimated to a temperature that closely matches the mean
growing season temperature. In this way it was made sure that the calculated temperature-standardized respiration of adapted organisms is associated with the mean
growing season temperature at the region of origin.
To determine the additional effect of adaptation temperature on the acclimation
temperature it is necessary to standardize the short-term temperature response and
the acclimation temperature. Therefore the temperature-standardized respiration of an
organism acclimated to 20°C (ln R20 ACC 20) is used for standardization. The temperature-standardized respiration of an organism acclimated to 20°C is calculated from the
slopes of the temperature-standardized respiration to the inverse of acclimation
temperature for each organism.
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Darwin & Arrhenius
Statistics
By ln-transforming the data we obtained a linear relation between basal metabolic
rate and the inverse of temperature for non-acclimation, acclimation and adaptation.
As the samples of the animal or plant species are not randomly distributed over the
full range of adaptation temperatures, the effect of measuring temperature and acclimation temperature can be biased. Using a factorial ANCOVA, we ensured that the
short-term and acclimated temperature responses for each species and for animal or
plant species are independent of adaptation temperature.
Temperature effects in the non-acclimation and acclimation data subsets were
investigated with a GLM on the homogeneity of slopes to test if the effect of temperature is different for different species and subsequently with an ANCOVA to determine
the correlation of the ln-transformed respiration using the inverse of temperature
(1/kT or 1/kTACC) as covariate and “species” as the categorical factor (58 levels for
non-acclimation and 32 for acclimation). Furthermore we also tested for significant
species and temperature interactions of plants or animals separately by a GLM on the
homogeneity of slopes and an ANCOVA. The adaptation data subset is tested by a GLM
on the homogeneity of slopes and an ANCOVA using the inverse of adaptation temperature (1/kTAD) as covariate and “animal/plant” as the categorical factor to determine
the dependence on adaptation temperature for animals and plants separately.
In the second step, we created a new data set combining the activation energy of
the non-acclimation and apparent activation energies of the acclimation and the
slopes of the adaptation data of either animals or plants. Using a full-factorial two-way
ANOVA with “animal/plant” and “data subset” as fixed factors we tested for significant
differences in the temperature-dependence between the three data categories for
plants and animals separately.
For the analysis of the effect of acclimation temperature on the temperature-standardized respiration rate (ln R20) we applied a GLM on the homogeneity of slopes and
an ANCOVA using the categorical factor “species” and the inverse of acclimation
temperature (1/kTACC) as covariate. In addition we applied a GLM on the homogeneity of slopes and an ANCOVA for animal or plant species separately.
The additional effect of adaptation temperature on the temperature-standardized
respiration of organisms acclimated to 20°C (ln R20 ACC 20) is determined by a GLM on
the homogeneity of slopes and an ANCOVA where the correlation of the temperature
standardized respiration of an organism acclimated to 20°C is tested on the adaptation
temperature (1/kTAD) for the categorical predictor “animal/plant” and differences in
the dependency of animal and plant species.
Furthermore we designed a GLM on the homogeneity of slopes and an ANCOVA
using “animal/plant” as categorical predictor to determine the dependence of the plasticity (given by the slope of the temperature-standardized respiration to the acclimation temperature for each species) on the adaptation temperature (1/kTAD).
For the analysis of the effect of adaptation temperature on the temperature-standardized respiration for animal or plant species a GLM on the homogeneity of slopes
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Chapter 2
and an ANCOVA using “animal/plant” as categorical factor are performed to determine the correlation for the two the taxonomic groups, animals and plants and differences between them.
All statistical analysis was done using Statistica 7.
-4
A
-5
-6
-7
-8
-9
-10
-11
ln respir ation (Watt/g3/4)
-4
B
-5
-6
-7
-8
-9
-10
-11
-4
C
-5
-6
-7
-8
-9
-10
-11
37
38
39
40
41
temperature (1/kT in 1/°K)
26
42
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Darwin & Arrhenius
Results
Short-term temperature responses are Arrhenius-like, with species-specific
activation energies
The results of our metadata analysis on short-term, acclimated and adapted temperature effects are presented in the form of Arrhenius plots, where the natural logarithm
of the biomass-standardized basal metabolic rate (ln R in Watt/g3/4) is plotted against
ANIMALS
Aneides flavipunctatus
Aneides lugubris
Antrops truncipennis
Batrachoseps attenuatus
Bolitoglossa occidentalis
Bunopus tuberculatus
Calanus finmarchicus
Calanus glacialis
Calanus hyperboreus
Cherax tenuimanus
Crangon septemspinosa
Damon annulatipes
Desmognathus quadramaculatus
Ensatina eschscholtzii
Eurycea longicauda
Exallias brevis
Glossina morsitans
Gyrinophilus danielsi
Gyrinophilus porphyriticus
Hophlosphyrum griseus
Hydromantes spp.
Hydromedion sparsutum
Karoophasma biedouwensis
Kinosternon subrubrum
Metridia longa
Metridium senile
Myoxocephalus scorpius
Neoceratodus forsteri
Notothenia negleeta
Onychiurus arcticus
Paracirrhites arcatus
Paracirrhites forsteri
Paractora dreuxi
Paractora trichosterna
Penaeus monodon
Perimylops antarcticus
Perna perna
Plethodon glutiosus
Porcellio laevis
Pseudoeurycea gadovii
Pseudoeurycea goebeli
Pseudotriton ruber
Ptyodactylus hasselquistii
Rana catesbeiana
Sauromalus hispidus
Sebasticus marmoratus
Solenopsis invicta
Thamnophis sirtalis
Thorius sp.
Trechisibus antarcticus
Varanus gouldii
PLANTS
Acacia mealnoxylon
Acanthophora spicifera
Chamaecyparis obtusa
Chondrus crispus
Colobanthus quitensis
Deschampsia antarctica
Egeris densa
Eucalyptus camaldulensis
Gossypium hirsutum
Hydrilla verticillata
Laminaria saccharina
Magnolia grandiflora
Milicia excelsa
Pelagonium hortorum
Petunia hybrida
Pinus radiata
Pisum sativum
Plantago euryphylla
Plantago lanceolota
Plantago major
Poa costiniana
Poa trivialis
Rumex palustris
Sempervivum montanum
Silene dioica
Silene uniflora
Sorghum bicolor
Sphagnum subsecundum
Tagetes patula
Trema guineensis
Triticum aestivum
Vicia faba
Viola wittrockiana
Betula papyrifera
Larix laricina
Picea mariana
Pinus banksiana
Populus tremuloides
Figure 2.2 (left) Arrhenius plots of the biomass-normalized respiration (ln R in Watt/g3/4)
against inverse temperature (1/kT in 1/eV) for (A) short-term temperature (B) intermediate
(acclimation) temperature and (C) long-term (adaptation) temperature exposure. Each symbol
represents one and for all plots the same species (see in legend), furthermore we separated the
symbols to animal species (blue/black) and plant species (green/grey). Statistics are given in
Table 2.1 & 2.2.
(A) The slope of the short-term temperature response for each species represents the activation
energy (E) of an individual acclimated to a specific temperature and respiration measured at
different temperatures. The variation of the activation energies across species is shown in Figure
2.3A. In addition the broad range of the respiration at 20°C (≈ 39.5 1/eV, temperature-normalized respiration) shows that the short-term temperature response differs between species.
(B) The slope of the acclimated temperature response show the acclimated apparent activation
energy (EACC) of a species, where individuals are acclimated to the respiration measured temperature. The variation of the acclimated apparent activation energies is shown in Figure 2.3B.
(C) The slopes of the adapted temperature response represent the adapted apparent activation
energy (EAD) of plants and animals. The high variability of the respiration of organisms acclimated and measured at the mean growing season in the region of origin shows that other biotic
and abiotic factors beside temperature are determinant for the respiration.
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Chapter 2
no of observations
20
A
16
12
8
4
0
>0.9 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
activation energy (eV)
no of observations
10
plant species
animal species
B
8
6
4
2
0
>0.9 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
acclimated apparent activation energy
(EACC in eV)
Figure 2.3 Frequency distributions of the slopes of the respiratory temperature responses that
reflect: (A) the average activation energies of the limiting enzymes of the metabolic network of
species by short-term temperature exposure duration and (B) the acclimated apparent activation
energy by intermediate temperature exposure duration, resulting in differences in the enzymatic
network..The differences in the activation energies of the short-term temperature response and
apparent activation energies of the acclimated temperature response show that by acclimation
the slope of the temperature response differs.
the reciprocal of temperature (1/kT). The plots for the short-term and acclimation
effects show the response for each species at different temperatures, while for the
adaptation response the respiration of different species at the temperature in the
region of origin is plotted. The slopes reflect the temperature response of the basal
metabolic rate of the short-, intermediate- and long-term temperature exposure duration (Figure 2.2). Additionally, the histograms show the distribution of the speciesspecific activation energies derived from the short-term temperature-dependence and
apparent activation energies derived from the respiration rates at the different acclimation temperatures for the individual species (Figure 2.3).
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Darwin & Arrhenius
The short-term temperature-dependence of the basal metabolic rate for each
species is strongly related to temperature. The exponential increase in basal metabolism with temperature shows that for short-term exposures, the temperature-dependence of the basal metabolism for each species can indeed be described by the
Arrhenius relation (Figure 2.2A, Table 2.1). The activation energy, however, varies
between species and ranges from 0.27 to 1.12 eV (Figure 2.3A, Table 2.1). The activation energies vary also between animal and plant species (Table 2.1). These speciesspecific differences in the activation energies indicate that either the properties of a
common rate-limiting enzyme differ between species or that in the enzymatic
networks of different species the key-enzyme varies.
Acclimation and adaptation result in a lower temperature-dependence of the
metabolic rate
The basal metabolic rate of organisms acclimated to different temperatures, depends
on the acclimation temperature (Figure 2.2B). The natural logarithm of the basal
metabolic rate is linearly related to the inverse of the acclimation temperature (Table
2.1). Different plant species do differ in their apparent activation energy (EACC). In
contrast, although we have to take into consideration that the sample size is very
small, this was not found for animal species (Figure 2.3B, Table 2.1).
Testing the relation for all species combined, the acclimation temperature-dependence of the acclimated basal metabolic rate is statistically different from the thermodynamic, short-term, temperature response. When this was tested separately for animal
Variable
Dependent
nonacclimation
acclimation
adaptation
ln respiration
ln respiration
ln respiration
Analysis
Independent
Multivariate
ANCOVA
Categorical Continuous
Df
species
58
66.51 <0.0001
57 4.34 <0.0001
1/kT
F
p
Homogeneity
of slope
Df
F
p
animals
1/kT
38
44.58 <0.0001
37 2.91 <0.0001
plants
1/kT
20 135.28 <0.0001
19 9.01 <0.0001
31 3.56 <0.0001
species
1/kTACC
32
17.22 <0.0001
animals
1/kTACC
7
28.20 <0.0001
plants
1/kTACC
25
9.74 <0.0001
animal/
plant
1/kTAD
1
3.06
0.10
6 0.71
0.6503
24 2.81 <0.003
1 1.06
0.31
Table 2.1 Statistical analysis of the short-term, acclimated and adapted temperature response.
The ANCOVA predicts the dependences of the logarithmical transformed respiration on the shortterm exposure, acclimated or adapted temperature for each species, for plant and/or animal
species included in the test (p<0.05). The GLM on the homogeneity of slope predicts if the
temperature-dependence is the same across all species, for animal or plant species (p>0.05).
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Chapter 2
or plant species, for animal species the apparent activation energies of the acclimated
temperature response are not different from the activation energies of the short-term
temperature response. In contrast, for plant species the apparent activation energies
are significantly lower than the activation energies (Table 2.2). This shows that by
acclimation plants can adjust to the prevailing temperature, while animals seem to
exhibit low plasticity (Figure 2.3B).
-5
A
-6
-7
-8
ln respiration at 20°C (Watt/g3/4)
-9
-5
B
-6
-7
-8
-9
-5
C
-6
-7
-8
-9
38
39
40
41
temperature (1/kT in 1/°K)
30
42
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Darwin & Arrhenius
In temperature adapted organisms the apparent activation energy (EAD), for
animals and plants tested separately, is independent of temperature (Table 2.1).
Although the respiration of animals and plant at the growing season temperature
exhibits a high variation, it can be concluded that basal metabolism is not strictly
controlled by body mass and temperature alone (Figure 2.2C).
The temperature-standardized respiration, the calculated respiration at 20°C, is
negatively related to the inverse of the acclimation temperature for each species
(Figure 2.4A, Table 2.3). This implies that the lower slope of the acclimated temperature response is, at least partly, the result of acclimation-induced changes in the
Non-acclimation
Acclimation
Adaptation
species
-0.58 a (±0.17)
-0.39 b (±0.23)
-0.21 b (±0.08)
animals
-0.61 a (±0.15)
-0.62 a,b (±0.16)
-0.27 b
plants
a,b
-0.52
a
(±0.18)
-0.33
b
(±0.21)
-0.15 b
significant different
Table 2.2 Average activation energy of the short-term and the average apparent activation energies of the acclimated and adapted temperature response for all species and for animal or plant
species. The lower apparent activation energies of the acclimated and adapted temperature
response indicate that the acclimated and adapted temperature response differs from the shortterm temperature response. Significant levels ± 95% confidence interval are shown in the table.
Figure 2.4 (left) Arrhenius plots of the temperature-normalized respiration given by the logarithm of the respiration at 20°C (ln R20) to the inverse acclimation or adaptation temperature.
Each symbol represents one and for all plots the same species, animal species (blue/black) and
plant species (green/grey). The species represented by the symbols are identical to Figure 2.2
(legend Figure 2.2). The plots show the differences in the temperature and biomass-normalized
respiration
(A) of species adapted to different temperatures, where the temperature- and biomass-normalized respiration of a species at different adaptation temperatures reflects the strength of genetical
modifications (G).
(B) of a species acclimated to different temperatures. The difference in the temperature-normalized respiration of a species at different acclimation temperatures reflects its plasticity (P).
(C) of species acclimated to 20°C and adapted to different temperatures, showing that the adaptation temperature has an additional impact on the temperature-normalized respiration beside
the acclimation temperature.
The positive correlation of the temperature-normalized respiration to the inverse of acclimation
or growing season temperature in (A) and (B) shows that by acclimation and/or adaptation to
high temperatures the temperature and biomass-normalized respiration decrease. Thus, the
lower acclimated and adapted temperature response compared to the short-term temperature
response can be described by occurring changes in the temperature-normalized respiration of the
short-term temperature response.
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Chapter 2
temperature-standardized respiration. Comparing the change in slope of different
species shows that this is highly variable across species (Table 2.3). Thus the strength of
the plastic response to temperature is, in contrast to the resultant apparent activation
energies, similar between species. So, in acclimated animals, where no change in
apparent activation energy was found, the basal metabolic rate is modified by lowering
the temperature-standardized respiration with increasing acclimation temperature
(Table 2.3).
For organisms acclimated to 20°C (ln R20 ACC 20) the temperature standardized
respiration (ln R20) depends significantly on the temperature in the region of origin for
all species and for both animal and plant species tested separately (Figure 2.4B, Table
2.3).
The reaction norm of the plastic reaction (the changes in the metabolism by acclimation) of organisms did not depend on the temperature in the region of origin for
different organisms and neither for different plant species separately. Thus the ability
to acclimate is independent of adaptation temperature (Table 2.3).
The calculated temperature-standardized respiration is for both animal and plant
species adapted to high temperature significantly lower, than for those adapted to low
temperature (Figure 2.4C, Table 2.3). The significant change in the temperature-standardized respiration with growing season temperature indicates that this is a mechanism by which the metabolic rate in adapted organisms has become independent of
the growing season temperature in the region of origin. Thus the adaptation temperature has, like the acclimation temperature an impact on the temperature standardized
respiration of organisms, counteracting the short-term temperature response.
Variable
Dependent
Independent
Categorical
acclimation
adaptation
ln R20
Analysis
Continuous
Multivariate
ANCOVA
Df
F
p
Homogeneity
of slope
Df
F
p
species
1/kTACC
1
5.55 <0.0005
13
1.48
0.21
animals
1/kTACC
1
5.77 <0.001
1
0.99
0.42
plants
1/kTACC
1
ln R20 ACC 20 animal/plant
1/kTAD
1
4.84
0.17
Plasticity
animal/plant
1/kTAD
1
0.18
ln R20
animal/plant
1/kTAD
1
7.11 <0.005
13.13 <0.005
0.83
11
1.53
0.21
1
3.10
0.12
1
0.28
0.61
1
1.85
0.18
Table 2.3 Statistical analysis of the change in the temperature-normalized respiration by acclimation and adaptation, the additional impact of adaptation temperature on the temperaturenormalized respiration by acclimation and the impact of adaptation temperature on the plasticity
(given by the change in the temperature-normalized respiration by acclimation). The ANCOVA
predicts if the dependent factor is related to the continuous factor for the various categories
(p<0.05). The GLM on the homogeneity of slope predicts if the temperature-dependence is
similar between the categorical factors (p>0.05).
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Darwin & Arrhenius
Discussion
By short-term exposure to high temperatures respiration increases while exposure to
colder temperatures lead to decreased respiration. The exponential change in the
metabolic rate confirms that the Arrhenius relation which describes the temperature
response of single enzymes can be applied to determine the short-term respiratory
temperature-dependence of organisms (Aleksiuk 1971; Brechignac & Furbank 1987;
Hirche 1987; Somme et al. 1989; van Iersel & Lindstrom 1999).
The comparison of the short-term temperature responses of different organisms
shows that the activation energy appears to be species-specific. Other studies showed
that the energies are similar between taxonomic groups (Gillooly et al. 2001; LopezUrrutia et al. 2006). However, to determine the activation energy it is necessary to
measure the short-term respiratory temperature response intraspecifically. This
approach will exclude the effects of acclimation and adaptation temperature on the
temperature-standardized respiration, a precaution that was not taken by Gillooly and
co-workers (Clarke 2004). Other studies that determined the activation energy
intraspecifically also show that the activation energy is species-specific within taxonomic groups (Grigg et al. 1998; Terblanche et al. 2007; Wallace & Jones 2008).
Another prediction of the MTE is that, the basal metabolic rate of organisms can be
explained by physical and chemical principles so that the Arrhenius relation is
assumed to describe the universal temperature-dependence (UTD) of all organisms. In
contrast, our results show that the thermal life history of organisms results in differences in the respiratory temperature-dependences. The lower apparent activation
energies of organisms acclimated to different temperatures and the temperature independence of species adapted to the temperature of the region of origin, support earlier
results that demonstrated a compensatory effect of acclimation and adaptation on the
direct effect of temperature on the enzyme kinetics (Tjoelker et al. 1999; Xiong et al.
2000; Atkin & Tjoelker 2003; Gifford 2003; Terblanche et al. 2005; Atkin et al.
2006a). The extent of the plasticity, and probably also time-dependence of the acclimated response is species-specific. In conclusion, the Arrhenius relation can be used to
describe the short-term temperature response of organisms, but can not be used to
predict the basal metabolic rate of organisms acclimated or adapted to a certain
temperature.
Our results show that both acclimation and adaptation affect the temperature-standardized respiration of an organism. An organism acclimated to high temperatures has
a lower temperature-standardized respiration, than the same organism acclimated to
cold temperatures. That acclimation to high temperature results in a lower temperature-standardized respiration and acclimation to cold temperature in a high temperature standardized respiration (Figure 2.1) is also shown in studies that focus on
differences of the short-term temperature response and acclimated temperature
response (e.g. Atkin & Tjoelker 2003; Terblanche et al. 2007). The same effect on the
temperature-standardized respiration is also found for organisms adapted to different
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Chapter 2
temperatures. Differences in the temperature-standardized respiration by adaptation,
although only within taxonomic groups, were also found in other studies (Feder 1976;
Chown & Gaston 1999; Clarke & Johnston 1999). The observed acclimation- and
adaptation-induced changes the temperature-standardized respiration could be the
result of modification in enzyme and/or substrate concentrations or of changes in
expressed enzyme isoforms. In the present study we show that across various species
and taxonomic groups the temperature-standardized respiration is dependent on the
adaptation temperature.
The adaptation-dependent changes in the temperature-standardized respiration
can be formalized by introducing a parameter that describes the counteracting effect
on the short-term temperature-dependence of organisms. The positive slopes of the
acclimated and adapted temperature responses show that organisms do not overcompensate. Thus the plasticity (P) and the strength of genetical modifications (G) are
both within a range of 0 to E like predicted.
The extent to which an organism exhibits acclimation-induced changes in the
temperature standardized respiration can be taken as a measure of the plasticity of a
species and proved to be a highly variable, species-specific trait. Contrary to another
study that reported the plastic response of a species to be stronger with increasing
adaptation temperature (Knies et al. 2006), plasticity did not correlate to the adaptation temperature of an organism in the present study. Possibly the discrepancy is due
to the use of intraspecific data, comparing an organism adapted for a few generations
by Knies and co-workers (2006), while our results are based on interspecific data and
focus on organisms that are adapted over multiple generations.
The acclimation temperature-induced, plastic, effect on the temperature-standardized respiration was found to depend on the adaptation temperature. As predicted in
the introduction of the framework the effect of the acclimation temperature should
depend on the difference between acclimation temperature and the temperature in the
region of origin.
Both adaptation and acclimation processes result in feedback control on temperature-induced changes in the basal metabolic rate, leading to a relative temperature
independence of metabolism between species. This result has implications for the
understanding of ecosystem functioning. Ecosystems of various climate zones have
been shown to follow the Arrhenius temperature-dependence at short-term, i.e. like
diurnal, temperature changes. However, comparing ecosystems of different climates,
metabolism (but also other metabolic processes like photosynthesis and growth rate)
are shown to be independent of the mean annual or mean growing season temperature (Enquist et al. 2003; Kerkhoff et al. 2005; Enquist et al. 2007).
The high variation in the temperature-dependence of adapted organisms and the
temperature-standardized respiration at different adaptation temperatures show that
the metabolism of organisms is not simply determined by biomass and the short-term,
acclimation and adaptation temperature. Other ecosystem limitations and trade offs
are equally important for the basal metabolic rate (Chown & Gaston 1999; Angilletta
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Darwin & Arrhenius
et al. 2003; Chown et al. 2003; Clarke 2003). The metabolism of organisms is not just
dependent on temperature, but also on other ecosystem restrictions like nutrient, food
and water availability (Mitchell et al. 1999; Tissue et al. 2002; Wright et al. 2006).
Further studies are needed on the dependency of the basal metabolic rate on temperature, on the effect of acclimation duration and on more different species. But it is
equally important to determine differences in the basal metabolic rate related to other
trade offs and environmental restrictions.
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Box 1
Variations in the effect of temperature
on respiration: The impacts of thermal
acclimation, adaptation and biomass
on the activation energy
Cordula Schmitz
J. Theo M. Elzenga
Species differ in the short-term effect of temperature on the metabolic rate, a reflection of the variation in the enzymatic networks of organisms (Davison et al. 1991).
The short-term temperature-dependence of the basal metabolic rate (B) is described
by the temperature standardized respiration (b0) and the Arrhenius-term e–E/kT
(Chapter 2) where T is the absolute temperature (in °K), E is the activation energy (in
eV) and k is the Boltzmann constant (8.62 * 10-5 eV/°K). Differences in the enzymatic
network of species can result in differences in metabolic rate at one and the same
temperature, expressed by the standardized respiration, b0, (Chapter 2). Differences
can also result in variations of the activation energy (E), the sensitivity of the metabolic rate to short-term temperature changes (Atkin et al. 2000a). Thermal acclimation and adaptation, counteracting the short-term temperature response of the
respiration rate, can be acting on both the standardized respiration ,b0, (Clarke &
Johnston 1999; Hochachka & Somero 2002) and the activation energy, E (Davison et
al. 1991; Atkin & Tjoelker 2003). By thermal adjustment the metabolic rate of an
organism becomes relatively independent of the ambient temperature. Short-term
changes in temperature, however, will still result in changes in the metabolic rate.
When the activation energy is low, short–term changes in temperature will have a low
effect on the metabolic rate, so that fluctuations in the metabolic rate are small. The
adjustment of the metabolic rate by acclimation and adaptation indicate that low fluctuation, low activation energy, is favorable (Chapter 2).
In organisms acclimated to different temperatures the activation energy varies;
high acclimation temperatures correlate with low activation energy (Atkin et al. 2003;
Davison et al. 1991). At the molecular level acclimation temperature effects on the
activation energy can for instance be due to differences in membrane fluidity (Pike &
Berry 1980; Berry & Raison 1981; Hazel 1995). The effect of thermal adaptation on
the activation energy is unclear. Both the prevailing temperature in the region of origin
and the amplitude of diurnal or seasonal temperature fluctuations might lead to
adjustment of the activation energy. Here we present a metadata analysis (Chapter 2)
on the effects of thermal acclimation and adaptation on the activation energy.
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Variations in the activation energy
Firstly, we examine the effects of acclimation temperature (between individuals of
a single species) and adaptation temperature (between different species) on the activation energy (Figure Box 1.1, Table Box 1.1). Changing the acclimation temperature
has an effect on activation energy in plants, but not in animals. The absence of a statistically significant effect of acclimation temperature in animals might be due to the
small sample size, only four species, in the dataset used. The variations in the activation energies at different acclimation temperatures for plants are species-specific
(Table Box 1.1). For some cold adapted plant species the activation energy is
decreasing and for other species we found an increase up to a certain temperature and
a slight decrease at higher temperatures (Figure Box 1.1). The observed dependence
of the activation energy on acclimation temperature confirms earlier studies (Davison
et al. 1991; Atkin et al. 2003; Atkin et al. 2006a). However, these studies only describe
that the activation energy increases with increasing acclimation temperature. Here we
show that the change in activation energy at different acclimation temperatures is
variable.
To quantify the effects of adaptation temperature on the activation energy, a
comparison was made between species from different climatic regions. The activation
energy appears to be independent of growing season temperature for all species, and
also for plants and animals separately (Table Box 1.1). Thus variations in the activation energy like e.g. caused by changes in the membrane fluidity at different acclimation temperatures do not occur across species adapted to different temperatures. This
adaptation temperature-independence might result from the adjustment of the transition temperature of the cell membrane, a property determining membrane fluidity
(Pike et al. 1980; Berry et al. 1981; Hazel 1995).
ANIMALS
Glossina morsitans
Metridium senile
Pema pema
Rana catesbeiana
activation energy (eV)
-0.1
PLANTS
-0.3
-0.5
-0.7
-0.9
4
8
12
16
20
24
acclimation temperature (°C)
28
32
Colobanthus quitensis
Deschampsia antarctica
Eucalyptus camaldulensis
Gossypium hirsutum
Laminaria saccharina
Pinus radiata
Pisum sativum
Plantago euryphylla
Plantago lanceolota
Plantago major
Sorghum bicolor
Triticum aestivum
Vicia faba
Figure Box 1.1 Acclimation temperature-dependence of the activation energy of a species. The
activation energy (E) for each species represented by one and the same symbol in Figure Box 1.1
& Box 1.2. Closed symbols represent animal species and opened symbols plant species. The activation energy (E) is linear related to acclimation temperature, the regression lines show the relations for a selection of species. The statistics are given in Table Box 1.1.
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Box 1
TACC E minimal (°C)
30
20
10
0
0
5
10
15
20
25
30
growing season temperature (°C)
Figure Box 1.2 Relation of the acclimation temperature where the activation energy is lowest to
average growing season (adaptation) temperature for plants. Each symbol represents a plant
species, one and the same given in Figure Box 1.1 (legend). The statistics are given in Table
Box 1.1.
In plants the activation energy seem to be optimized to the temperature in the
region of origin as the acclimation temperature, the activation energy is lowest is
related to the growing season temperature (Figure Box 1.2, Df = 1, F = 32.90,
p < 0.001). The acclimation temperature where the activation energy is lowest is
higher than the average growing season temperature. This might result from the
difference in high day and low night temperature. Respiration in plants is related to
photosynthesis, occurring at day temperature (Ryan 1991), so that respiration should
be related to day-time temperature, too.
Variable
Dependent
Analysis
ANCOVA
Simple regression
Predictor
Categorical
Continuous
Homogeneity
of slope
Df
F
p
Df
17
6.08 <0.001
12
7.46 <0.001
E
species
TACC
18
6.62
<0.001
E
animal species
TACC
5
3.43
0.128
E
plant species
TACC
13
8.26
<0.001
E
species
TAD
1
0.10
0.754
E
plant/animal
TAD
2
0.72
0.493
F
p
Table Box 1.1 Statistical analysis of the activation energies (E), given by the slope of the shortterm temperature-dependence of the metabolic rate, for all species, plant species or animal
species. The ANCOVA predicts the dependence of the activation energy (E) on the acclimation
(TACC) for a single species or adaptation (TAD) temperature between species. The relation is
significant for p<0.05. The GLM on the homogeneity of slope predicts, significant differences
(p<0.05) in the observed relations.
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Variations in the activation energy
In addition, the variation in activation energy at different acclimation temperatures
might differ between plants. E.g. high seasonal fluctuations, typically found in colder
climates are accompanied by high variation in the activation energy (Barry & Chorley
1992). To keep the activation energy more stable, for plants adapted to high seasonal
fluctuations the change in activation energy by acclimating to different temperatures
might be low. However, our results do not confirm this hypothesis (Df = 1, F = 1.76,
p = 0.25). Thus the activation energy is independent of seasonal temperature fluctuations.
Further, short-term temperature fluctuations might lead to adaptive differences in
the activation energy. Strong short-term temperature fluctuations occur by large differences between day and night temperatures (Barry et al. 1992). In our data set strong
variations between day and night temperature are associated with species originating
from higher latitudes and thus from colder climates. As we have seen already, the activation energy is independent of adaptation temperature, and therefore also not influenced by diurnal temperature fluctuations (Table Box 1.1). Second, temperature
fluctuations in different habitats might lead to changes in activation energy. Aquatic
ecosystems, characteristically experience relatively small fluctuations in temperature
compared to terrestrial ecosystems (Barry et al. 1992). As our metadata contain
aquatic animals, but not aquatic plants, we tested aquatic versus terrestrial animals,
but did not observe differences in the activation energy (Table Box 1.2). Another
possibility for differences in the activation energy is offered by comparing organisms
that differ in their lifestyle. Mobile organisms should encounter less temperature fluctuation, than sessile organisms like plants, sessile aquatic animals and plankton.
Mobile terrestrial species can escape less favorable temperatures by sheltering. Mobile
aquatic organism, like nekton and benthic fish, can move between water layers with
Variable
Analysis
Dependent
Predictor
Df
F
p
E
aquatic/terrestrial animals
1
0.26
0.612
E
sessile/mobile aquatic animals
1
9.46
0.356
E
sessile/mobile terrestrial organisms
1
7.24
<0.05
E
animals - ln biomass
1
11.12
<0.005
E
plants - ln biomass
1
0.12
0.738
E
taxonomic group
12
4.18
<0.005
Table Box 1.2 Statistical analysis of the activation energies (E) for different in ecosystems, traits
and biomass. For aquatic and terrestrial animals as well as for analysing taxonomic groups we
applied an ANOVA and to analyse the combined effect of aquatic or terrestrial organisms divided
to sessile and mobile we used a main-effect ANOVA. The categories are significant different for
p<0.05. For the dependence of the activation energy (E) for plants or animals on ln biomass we
applied a simple regression. The p<0.05 indicate, that the relation is significant
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Box 1
activation energy (eV)
-0.3
-0.5
-0.7
animals
plants
-0.9
-8
-4
0
4
8
ln biomass (g)
Figure Box 1.3 Biomass-dependence of the activation energy (E), at the growing season temperature in the region of origin. For animals the linear regression shows the dependence of activation energy (E) on biomass (M), in contrast for plants the activation energy (E) is independent of
biomass. Statistics are given in Table Box 1.2.
different temperatures. Our results show that the activation energies do not differ
between mobile and sessile aquatic animals, but they do differ between mobile and
sessile terrestrial organisms (Table Box 1.2). As mobile and sessile aquatic organisms
do not differ in their activation energy the observed effects for terrestrial organisms
showing differences between animals and plants might result either from the larger
fluctuations that can be encountered in terrestrial ecosystems or from differences
between taxonomic groups (see below).
Thirdly, biomass might be important. Large organisms are less affected by differences in the surrounding temperature, because with increasing biomass the surface to
volume ratio of an organism decreases (Grigg et al. 1998; Wallace & Jones 2008). This
difference in surface to volume ratio is important in the heat exchange rate of organisms, and possibly body temperature fluctuations. We examined animals and plants
separately, since for animals the external surface ratio decreases with increasing
biomass, while for plants this size-dependences of the external surface to volume ratio
is under debate (Niklas & Enquist 2002; Milla & Reich 2007). Our results show that
for animals the activation energy increases with biomass, but not for plants (Figure
Box 1.3, Table Box 1.2). Therefore small animals, which experience strong and
frequent fluctuations in body temperature benefit from having low activation energies.
Size-dependent differences in the activation energy of animal species have also been
shown in earlier studies (Grigg et al. 1998; Wallace et al. 2008). Importantly, these
results show that for determining the basal metabolic rate of animals it is necessary to
consider that activation energy is dependent on biomass and can not be treated independently, like has been the practiced (West et al. 1997; Glazier 2005).
Fourthly, our results show that differences in the activation energy between organisms depend on taxonomic group (Table Box 1.2, Figure Box 1.4), This conclusion was
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Variations in the activation energy
activation energy (eV)
0.0
-0.2
-0.4
-0.6
-0.8
sc
re
pt
ili
am
o
a
or m rph
pa p a
en hib
pe ifor ia
rc m
if e
co orm s
le e
op s
te
hy dip ra
m te
n r
cr opt a
us er
ta a
c
bi eae
va
cn lvia
pi idar
no ia
p
m lil sid
ag io a
p
no s
lio ida
ps
id
a
-1.0
taxonomic group (in phylogenetic order)
Figure Box 1.4 Differences in the average activation energy (E) for taxonomic groups, listed in
phylogenetic order. The symbols show the mean activation energy (E) and error-bars the 95%
confidence interval. The statistical analysis shows, that the activation energy (E) differs between
taxonomic groups (Table Box 1.2).
also reached by (Terblanche et al. 2007). Other studies showed that the energies are
similar between taxonomic groups (Gillooly et al. 2001; Lopez-Urrutia et al. 2006).
However, these studies determined the activation energy for a taxonomic group over
the whole range of data of each taxonomic group including organisms acclimated and
adapted to different temperatures. To determine the activation energy it is necessary
to use intraspecific data and exclude the effects of acclimation and adaptation temperature (Clarke 2004). Here, we showed that the activation energy is adjusted to the
thermal life history of a species, but varies for different acclimation temperatures.
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