Cold-acclimation in Peromyscus: temporal effects and individual variation in maximum metabolism and ventilatory traits
Department of Biology, University of California, Riverside, California 92521, USA
* Author for correspondence (e-mail: erezende{at}citrus.ucr.edu)
Accepted 20 October 2003
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Summary |
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Key words: acclimation, ambient temperature, maximal oxygen consumption, physiological plasticity, Peromyscus maniculatus, repeatability, thermogenesis, ventilation
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Introduction |
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For several reasons, thermal acclimation in small endotherms is a useful
system for studying physiological plasticity. First, it can be induced simply
by changing ambient temperature. Second, the response can be easily measured
as maximal rates of oxygen consumption
(O2max). Third,
thermal acclimation is ecologically relevant in highly seasonal habitats
(Rosenmann et al., 1975
;
Cygan, 1985
;
Zegers and Merritt, 1988
;
Hayes, 1989
;
Bozinovic et al., 1990
;
Merritt, 1995
;
Kronfeld-Schor et al., 2000
).
Fourth, there are considerable data on the mechanistic basis of thermal
acclimation at different levels of organization, from organ size (e.g.
McDevitt and Speakman, 1994
;
Speakman and McQueenie, 1996
;
Derting and Austin, 1998
;
Hammond and Kristan, 2000), to physiology and biochemistry
(Golozoubova et al., 2001
;
Nedergaard et al., 2001
;
Deveci et al., 2001
;
Shmeeda et al., 2002
), to gene
expression (Jacobsson et al.,
1994
; Yu et al.,
2002
). Finally, recent studies have found significant selection on
O2max in wild
populations, re-emphasizing its evolutionary and ecological relevance
(Hayes and O'Connor, 1999
; E.
L. Rezende, F. Bozinovic and T. Garland, unpublished results).
Despite considerable study, some aspects of thermal acclimation merit
additional work. There are few data on within-individual performance
consistency across acclimatory events
(Hayes and Chappell, 1990;
Nespolo and Rosenmann, 1997
).
Individual consistency (repeatability) over time is a prerequisite for natural
selection to affect trait variation, and it may set the upper limit on the
narrow sense heritability of the trait if certain conditions are fulfilled
(Hayes and Jenkins, 1997
;
Dohm, 2002
). Also of major
interest is the time course of acclimation - the latency of response to a
changed environment, and the time necessary for acclimation to reach a stable
end point. Besides its biological repercussions, the time course of
acclimation has practical ramifications. For comparative analyses of
acclimatory responses, it is necessary to know whether the end point of a
study represents completion of acclimation (i.e. a new physiological steady
state) or a time when physiology is still changing in response to
environmental change. For example, a brief survey of thermal acclimation
studies cited in this paper (see References) revealed a sevenfold range in
acclimation periods (2-4 weeks), and there were few controls on the progress
or completion of acclimation (e.g. Nespolo
and Rosenmann, 1997
). Thus, within a single individual or a single
species, it is difficult to come to conclusions about when acclimation is
actually complete.
Temporal patterns may also provide clues about the mechanistic
underpinnings of acclimatory responses. Presumably, acclimation requires
adjustment of multiple set-points in reaction to a new thermal environment;
given the complexity of potential changes at many integrative levels and the
likelihood of time lags between detection and responses, we hypothesized that
the kinetics of cold acclimation may include an `overshoot' of
O2max before
stable acclimation is achieved (as reported for Abrothrix andinus;
Nespolo and Rosenmann, 1997
).
Accordingly, we designed a high temporal-resolution study of cold acclimation
in the deer mouse Peromyscus maniculatus, a species with a strong
acclimatory response to cold (Hayes and
Chappell, 1986
). The experimental design was based on repeated
measures with controls for measurement artifacts, which allowed analyses of
the detailed temporal pattern of acclimation and the effects of acclimation on
individual consistency of body mass,
O2max and
several associated ventilatory traits (the initial stages of oxygen
uptake).
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Materials and methods |
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We needed repeated measurements from each mouse, but
O2max estimation
by acute cold exposure may itself induce acclimation
(Heimer and Morrison, 1978
).
To minimize this problem and control for its effects, we divided each
treatment group into three subgroups (N=7 per group, 4 and 3 of each
sex) that did not differ in age or size. (For simplicity, `groups' will be
used to make reference to the six subgroups [i.e. group effects, etc.], and
`treatments' will refer to the two different acclimatory regimes.) Each group
was measured once a week, and different groups within a treatment
(cold-acclimated or control) were measured every 2 days. This provided three
data points per treatment per week, but each individual was measured only once
per week (see Fig. 1).
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Two animals died for unknown reasons during acclimation, one in each treatment. One control mouse was not included in analyses because its Mb increased by 55.1% during the experiment, contrasting with the average increase in Mb of 3.7±11.7% (1.2±11.5% in the cold-acclimated group and 6.2±11.9% in controls).
Metabolism and ventilation
We measured
O2max in an
atmosphere of heliox (79% He, 21% O2), which is several-fold more
conductive than air (Chappell and Bachman,
1995
). The open-circuit system contained a Plexiglas metabolism
chamber (volume 600 ml) supplied with heliox at 1700 ml min-1
(maintained ±1% with a Tylan mass flow controller; Mykrolis
Corporation, USA). An environmental cabinet controlled the temperature of the
metabolism chamber. About 100 ml min-1 of excurrent gas was
diverted, dried and scrubbed of CO2 (Drierite® and Soda lime,
respectively), redried, and passed through an S-3A O2 analyzer
(Applied Electrochemistry; CA, USA). Flow rate, Ta, and
O2 concentration were recorded every second by a Macintosh computer
running `Labhelper' software
(www.warthog.ucr.edu).
Animals were placed in the metabolism chamber at a Ta
approx. -5°C and recording began as soon as the system was completely
flushed with heliox (approx. 1 min). Ta declined at a rate
of approx. 0.5°C min-1. We terminated measurements and removed
animals when
O2
remained below initial values for more than 1 min, or did not increase as
Ta declined by more than 2°C. Trials lasted no longer
than 15 min. Immediately after removing an animal from the chamber, body
temperature Tb was determined (±0.1°C) using a
rectal thermocouple connected to a Bailey BAT-12 (Sensortek Inc., USA)
thermometer. All
O2max tests were
performed between 09:00 h and 13:00 h (local time). Oxygen consumption
(
O2) was
calculated using equation 4a of Withers
(1977a
), and
O2max was
determined as the highest continuous average value of
O2 over a 60 s
period.
During O2max
trials we measured breathing frequency (f; Hz) and tidal volume
(VT; ml) using whole-body plethysmography (Withers,
1977a
,b
;
Bucher, 1981
;
Chappell, 1985
). Chamber
pressure changes due to warming and humidification of tidal air were recorded
with a pressure transducer (Omega PX 164-010; Omega Engineering, Inc.,
Stanford, USA) connected to the computer and sampled at 125 Hz. The system was
calibrated after each trial by injecting a known volume of heliox (1.0 ml)
into the chamber at rates matching the kinetics of inhalation cycles.
VT was calculated from calibration data and pressure changes during
inspiration according to Malan
(1973
); we assumed lung
temperature was 37°C (based on post-measurement Tb
data) and that air in the respiratory tract was 100% saturated with water
vapor. Oxygen extraction efficiency (OEE, %) was calculated as
100x
O2max/(0.2095VMIN),
where VMIN (minute volume) is fVT.
Analysis of acclimation effects
In most animal taxa the relationship between body mass and metabolism is
best fit by the power equation
O2
=aMbb
(Darveau et al., 2002
). Hence,
all statistical tests, with the exception of repeatability analyses, were
performed with log-transformed values of
O2max and
Mb (for simplicity, we refer to log-transformed data as
O2max and
Mb). Sequential Bonferroni adjustments
(Rice, 1989
) were employed to
control for Type I errors in multiple simultaneous tests. All analyses were
performed using SPSS for Windows.
Analyses of variance (ANOVA) and covariance (ANCOVA) were performed to
ensure that there were no differences between groups during the first 3 weeks
of measurements (Ta=23°C). Comparisons were done
between all six groups within a given week, and Mb was
included as a covariate for other traits. No between-group differences were
observed in Mb during the first 3 weeks
(P>0.640 in all weeks). However,
O2max was highly
variable during the first week, and significantly different among groups
(ANCOVA, F5,34= 3.452, P=0.013;
Fig. 1), whereas no differences
were observed during the second and third weeks (ANCOVA,
F5,34= 0.902, P=0.491 and
F5,34= 0.814, P=0.548, respectively). We believe
that variation in the first week was probably related to non-standardized
procedures and initial adjustment of animals to the experimental conditions.
Therefore, data from the first week of measurements was not included in other
analyses.
Temporal changes in
O2max and
ventilatory variables during acclimation were analyzed in two ways. First, we
used general linear mixed models for repeated measures (GLM), in which
individuals were experimental units with time as a within-subjects factor. We
employed Mauchly's sphericity test to determine if the variance-covariance
matrix of the repeated measure variables is circular in form [`sphericity' or
Huynh-Felt (H-F) condition; i.e. whether orthonormalized contrasts are
independent and have equal variances. This can be thought of as an extension
of the homogeneity of variance assumption in independent measures ANOVA].
Where the sphericity condition did not hold, P-values of
within-subject effects were reported with H-F adjustments, which basically
consist of discounting degrees of freedom by a factor proportional to the H-F
condition to be met (Littell et al.,
1996
). Sex and treatment were included as between-subject factors;
this allowed us to quantify the effects of cold-acclimation controlling for
sex (time x acclimation effect). To determine when physiological changes
occurred, contrasts (differences between successive weekly values for
individuals) were compared with multivariate ANOVAs (test of within-subjects
contrasts). Comparisons among contrasts were performed separately for each
treatment (cold-acclimated and control), with sex included as a fixed
factor.
Second, we assessed the effects of cold acclimation with separate ANCOVAs
similar to the preliminary analyses described above. We pooled data of all
subgroups in each treatment within each week of measurement (9 weeks in
total), and compared pooled weekly values between the two treatments.
Acclimation and sex were included as fixed factors and Mb
was included as a covariate. To study the relationship between
O2max and
ventilatory traits, we performed a similar analysis with
O2max as an
additional covariate.
Repeatability
We performed one-tailed Pearson product-moment correlations between values
measured at different weeks to determine repeatability. We used this approach
instead of the intraclass correlation coefficient to assess repeatability,
because we expected cold acclimation to change the mean values for many of the
traits measured (see Hayes and Jenkins,
1997). A drawback of this method is that only pair-wise
comparisons can be performed, necessitating adjustment of
if
repeatability is estimated over several intervals
(Rice, 1989
).
Pearson correlations were performed at 2, 6 and 10 weeks. At 2 and 10 weeks
the cold-acclimated group was fully acclimated, whereas at 6 weeks
cold-acclimated individuals were still increasing
O2max
(Fig. 1). Because different
groups were measured at different times in the course of acclimation, analyses
were initially performed by group (6 groups in total). However, sample sizes
were small within groups (N=6 or 7), decreasing statistical power,
and interpretation of results was complex (see Discussion). Therefore,
correlations were performed with values pooled per week (weeks 2, 6 and 10; 2
treatments in total, N=19 or 20 per treatment), on both
non-transformed traits (Mb differences are intrinsic in
this case) and mass-independent traits (residuals from least-square mass
regressions carried out separately for both initial and final measurements),
and a sequential Bonferroni was employed to control Type I errors.
As a second method to estimate inter-individual variation through
acclimation, product-moment correlations were performed between the average
values of traits measured during 2 weeks prior to acclimation (weeks 2 and 3)
and during the last 2 weeks of acclimation (weeks 9 and 10). Assessing
repeatability of average values increases the robustness of analyses because
potential effects of measurement errors are minimized
(Falconer, 1989;
Hayes and Jenkins, 1997
).
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Results |
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Acclimation effects and temporal dynamics
There were no sex differences in response to cold-acclimation in any of the
physiological traits. However, Mb responded differently to
acclimation depending on both sex and acclimation temperatures (time x
acclimation x sex effect, Table
2). In the cold-acclimation treatment, males increased
Mb while females reduced Mb. In
control mice there were no significant differences in Mb
between sexes (Fig. 2). Prior
to acclimation, there were no changes in
O2max in the
cold-acclimated group (contrasts of week 2 vs. week 3;
F1,18=0.521, P=0.480), and no differences between
the
O2max of the
cold-acclimation and control groups (Table
3). Cold-acclimation had a strong and significant effect on
O2max, with
final values about 34% higher than for warm-acclimated mice
(Fig. 1, Table 2). Within-subject
contrasts showed that cold-acclimation significantly affected
O2max from weeks
3 to 10 (F1,18>29.579, P<0.0001 in all
cases), and that the `overshoot' of
O2max during
week 8 (Fig. 1) was
statistically significant. Results from ANCOVA confirm that acclimatory
responses in
O2max were
significant by the first week of acclimation. Mb remained
a significant predictor of
O2max during
each week of the acclimation regime (Table
3).
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As for control mice, cold-acclimated mice showed a general trend of
decreasing VT and VMIN over time (with a concomitant
increase in OEE; Fig. 3). Tests
of between-subject effects in the repeated-measures analyses (i.e. testing the
overall effect of acclimation in each of the traits by comparing the two
treatments) showed that cold acclimation significantly increased f
compared to controls (F1,35=4.961, P=0.032), but
there was no significant effect of acclimation on VT, VMIN
and OEE (F1,35<3.805, P>0.059 in all
cases). These results should be considered cautiously, however. The
between-subject effect of acclimation in VMIN and OEE bordered
significance (P<0.1 in both cases), and because
Mb was not included in the model in this instance,
inter-individual differences in Mb could be accounting for
part of the between-subject variation. Within-subject effect analyses (where
Mb is implicitly included, given the repeated-measures
design) largely support this view: cold-acclimated individuals had
significantly higher VT, VMIN and OEE values than control
mice (Table 2, Figs
3 and
4). As for
O2max, the
changes in f were apparent shortly after the start of cold
acclimation (although these were not statistically significant after
Bonferroni adjustment; P<0.047 from weeks 4 to 10;
Fig. 3).
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When O2max
was included as a covariate, we observed a significant positive relationship
between VT, VMIN and
O2max (the
positive coefficient was apparent in partial plots from multiple regressions
analogous to the ANCOVAs reported here). However, there was no relationship
between
O2max
and either f or OEE (Table
4).
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Repeatability
Mb and absolute and mass-independent
O2max were
significantly repeatable in both control and cold-acclimated treatments when
compared between weeks 2 and 10 (Tables
5 and
6). However, mass-independent
O2max was not
repeatable between week 2 and the middle of cold acclimation
(Table 6). Product-moment
correlations on initial and final Mb,
O2max and
mass-independent
O2max (mean
values for weeks 2+3 and 9+10) were consistent with the results obtained when
mean initial and mean final values were employed to estimate repeatability
throughout acclimation (Fig.
5).
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Breathing frequency (f) was highly repeatable in all conditions (Tables 5 and 6). Both VT and VMIN were significantly repeatable in control mice throughout the experiment, but repeatability was abolished by cold-acclimation (there was no significant repeatability in VT or VMIN between pre- and post-acclimation tests). However, VT and VMIN were repeatable within the period of cold acclimation (week 6 vs. week 10; Tables 5 and 6). Inter-individual differences in OEE were not repeatable in either group (Table 5). However, when consistency of mean initial vs. mean final OEE was assessed, repeatability of OEE was significant in control (N=19, r=0.445, P<0.01), but not in cold-acclimated animals.
In general, cold-acclimation tended to decrease individual consistency (with the notable exception of f). When we compared all repeatability analyses, excluding f (Tables 5 and 6), repeatabilities were significantly higher in control mice than between pre- and post-acclimation in the cold-acclimated group (paired t-test, t26=2.864, one-tailed P<0.004).
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Discussion |
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There was no change in
O2max in our
control mice between the beginning and end of trials. That result contrasts
with the findings of Heimer and Morrison
(1978
), who reported a
significant training effect on
O2max in
warm-acclimated Peromyscus. The difference between the studies is
most likely due to different measurement protocols: Heimer and Morrison
measured
O2max
in heliox twice a week (instead of once per week in our protocol), and the
duration of their cold-exposure trials were almost twice as long as ours
(about 30 min; fig. 1 in their
study).
We did find training effects in ventilatory traits, but the magnitude of
the changes varied considerably between control and cold-acclimation groups
(Fig. 3); for example,
f increased by about 3.3% in control and 6.1% in cold-acclimated
animals. Mean initial and final f values in control groups (8.00 and
8.27 Hz) were substantially higher than those reported for Peromyscus
measured in air at -10°C (about 5.5 Hz;
fig. 1B in
Chappell, 1985). The difference
could be that Chappell's mice probably did not attain
O2max, and
because the physical properties of air and heliox are not identical
(Kudukis et al., 1997
).
Cold-acclimated mice largely accommodated a 34% elevation in
O2max by
increasing OEE, with little change in pulmonary convection. A similar response
has been reported in other species
(Mortola and Frappell, 2000
),
but many small mammals use different strategies (i.e. supporting increased
metabolism by increasing ventilation; Casey
et al., 1979
; Chappell,
1992
; Chappell and Dawson,
1994
). An increase in OEE permits greater aerobic metabolism (and
hence heat production) without compromising respiratory heat loss
(Chappell, 1985
;
Mortola and Frappell, 2000
).
In this context, it was particularly interesting that warm-acclimated
individuals reduced ventilation and increased OEE after repeated cold-exposure
trials even though they maintained a fairly constant
O2max.
Temporal changes during acclimation
Interestingly, our data revealed a significant `overshoot' of
O2max at weeks
4-5 of cold-acclimation, followed by a decline of about 6% to an apparently
stable final value attained in week 7 (Fig.
1). Given that a similar overshoot was found in a distantly
related rodent (Abrothrix andinus;
Nespolo and Rosenmann, 1997
),
and that most studies of acclimation responses had low temporal resolution
(i.e. they measured metabolism only at the beginning and end of the
acclimation period), this may be a general response to cold acclimation and
not a unique characteristic of Peromyscus.
We postulate that this overshoot in metabolism reflects the control of a
homeostatic status through negative feedback. Acclimation responses require a
continuous perception of a non-homeostatic status due to a new thermal
environment (information acquisition cost; sensu
DeWitt et al., 1998),
responding simultaneously in multiple levels of organization in an integrated
fashion (production costs), and finally resetting the set points of all traits
involved in order to maintain homeostasis (i.e. negative feedback
regulation).
Because of the complexity involved in modulating these responses, and the
intrinsic time lag present in any physiological system from the detection of a
particular stimulus to the overall response associated with it, we
hypothesized that `over-acclimation' (e.g. higher
O2max values
than required for a particular Ta) would occur, in an
analogous way to predictions of population sizes above carrying capacity when
delay is incorporated in the logistic equation
(Roughgarden, 1998
). Such an
equation is physiologically realistic (see
Fig. 6) and provides
interesting predictions worth testing, such as increased overshoots
concomitantly with (i) a higher contrast between acclimating temperatures
[hence, whether or not the overshoot is detected in a particular study will
depend not only on the frequency of sampling but also on (presumably) the
difference between pre and post-acclimation temperatures (18°C in this
study)] and (ii) increased acclimatory rates (everything else being equal).
The logistic curve also predicts that (iii) animals with low acclimatory rates
(r) would probably not show any overshoot during acclimation, as the
product of acclimatory rates x delay time (r
; see
Fig. 6) is low. In this
context, we would expect that species from highly seasonal environments would
have higher acclimatory rates than species from thermally stable
environments.
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Individual variation in
O2max and ventilatory
traits
Maximal oxygen consumption was highly repeatable over a period of 8 weeks
in both control and cold-acclimated mice, as previously shown for
O2max in
Peromyscus (Hayes,
1989
; Hayes and Chappell,
1990
). This means that an individual's relative performance
remains consistent even after absolute performance increased dramatically due
to acclimation; in other words, the proportional change in performance due to
acclimation was roughly the same in all individuals. However, repeatability
was lower while animals were still acclimating to cold conditions, suggesting
individual variation in rates of acclimation. Two of the three subgroups of
cold-acclimated mice showed higher product-moment coefficients between pre-
and post-acclimatory
O2max, with
considerably lower repeatabilities between the pre- and mid-acclimation
periods (Table 5).
Contrary to our expectations, the consistency of f was higher in
cold-acclimated animals than in controls. The opposite was true for
VT and VMIN; they remained repeatable in controls and were
not consistent during the initial stages of cold acclimation
(Table 5). However, consistency
of both VT and VMIN returned at the end of the acclimation
period. In general, these traits are more consistent during stable conditions
than during acclimatory change (Tables
5 and
6), as was true for
O2max
(Hayes, 1989
;
Hayes and Chappell, 1990
). In
contrast, an intermediate level metabolic index - daily energy expenditure
(Speakman et al., 1994
;
Berteaux et al., 1996
) - showed
substantial and inconsistent intra-individual variation. By studying traits
under conditions that maximize metabolic performance, researchers are most
likely to detect individual differences that might be under selection
(Berteaux et al., 1996
), such
as Hayes and O'Connor (1999
)
reported for
O2max in
Peromyscus.
Concluding remarks
Our results emphasize two important points that physiologists should take
into account when designing acclimation experiments. First, responses to
acclimation may be either under- or overestimated depending on when animals
are measured, and presumably the appropriate measurement time will vary
according to the species being studied. Hence, caution is warranted when
comparing thermal acclimation responses in different species, and particularly
when acclimation times are not consistent. One approach often used in
acclimation experiments is to acclimate for a `standard' period (e.g.
Heimer and Morrison, 1978;
Nespolo et al., 2001a
). That
approach (with the implicit assumption of consistent response rates) could
generate misleading conclusions if the kinetics of acclimation differed
between individuals or among groups or species (see above).
Second, acclimation rates differ among physiological traits. For example,
in our deer mice f showed rapid responses to acclimation and was
fairly stable by the third week of cold exposure, whereas
O2max did not
become stable until week 5 of cold exposure, and OEE continued to change until
close to the end of the 7-week acclimatory period;
Fig. 3). For many studies,
these problems can be ameliorated by allowing a sufficiently long acclimation
period for animals to attain a stable acclimated condition (see above) - but
the appropriate period can be firmly established only with detailed knowledge
of the temporal pattern of acclimation. Considering the high plasticity of
Peromyscus, our results suggest that an acclimatory period of about 2
months would be enough to ensure that animals are actually `acclimated' - and
not `acclimating' (see above).
Interestingly, the regulation of physiological plasticity could be under
selection and evolving - if this `trait' has a genetic basis. Different
species show highly contrasting responses to thermal acclimation (e.g. this
study; Nespolo et al.,
2001a,b
),
and it seems reasonable to expect that these species will vary in their
acclimatory responses at a temporal level as well. For instance, the fossorial
rodent Spalacopus cyanus showed extremely low acclimatory responses
in
O2max (11%
difference in
O2max at
temperatures of 15° and 30°C;
Nespolo et al., 2001b
). It was
impossible, however, to discriminate whether that is because (i) their
`set-points' in
O2max at these
temperatures was relatively narrow, (ii) their rates of thermal acclimation
(r in Fig. 6) were
extremely low, (iii) the delay (
in
Fig. 6) to respond to thermal
changes was high, or (iv) a combination of these factors. Hence, although in
both cases the outcome would be the same, it is not clear at which level
natural selection could be acting, and correlated responses in underlying
physiological traits might be considerably different. Furthermore, so far the
genetic background underlying each of these variables is not known, and it is
not clear which traits could evolve in this scenario when selection is acting
at the level of physiological plasticity. In summary, the question of how
acclimatory responses - and phenotypic plasticity, more generally - evolve
still remains poorly understood.
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Acknowledgments |
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