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First published online January 19, 2006
Journal of Experimental Biology 209, 466-474 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02024
A comparative analysis of thermogenic capacity and cold tolerance in small birds

Department of Biology, University of South Dakota, 414 East Clark Street, Vermillion, SD 57069, USA
* Author for correspondence (e-mail: dlswanso{at}usd.edu)
Accepted 1 December 2005
| Summary |
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Key words: thermogenesis, cold, bird, acclimatization, comparative analysis
| Introduction |
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Nevertheless, seasonal changes in shivering endurance and cold resistance
in some species of small birds may occur without corresponding changes in
Msum, and geographic variation in cold resistance is not
always associated with variation in Msum
(Dawson et al., 1983a
;
Swanson, 1993
). Thus, cold
tolerance and Msum do not always change in lockstep and
the extent of their phenotypic correlation is uncertain. Shivering endurance
and Msum are correlated intraspecifically in small birds
(Swanson, 2001
), but the
interspecific relationship between cold tolerance and Msum
has not been directly examined for birds. Intraspecific seasonal changes in
cold tolerance in birds are concluded either when birds tolerate a static cold
exposure longer in winter than in summer (e.g.
Dawson and Carey, 1976
;
Dawson and Smith, 1986
;
Cooper and Swanson, 1994
) or
when colder temperatures are required to induce hypothermia in winter than in
summer (Saarela et al., 1989
,
1995
;
Liknes et al., 2002
). Efforts
to test the interspecific relationship between cold tolerance and
Msum have not yet been undertaken, and are potentially
confounded by body size effects on metabolic rates and heat loss. Testing the
relationship between Msum and cold tolerance requires a
standardized cold exposure among species and measurement of either shivering
endurance or the temperature inducing hypothermia. Developing a standardized
measure of shivering endurance requires a standardized cold challenge for all
species measured, which is difficult, if not impossible, to attain because
factors such as body size and thermal conductance vary among species and
greatly impact heat loss to the environment
(Aschoff, 1981
). One way around
this problem, however, is to hold shivering endurance essentially constant
while measuring the temperature in helox (79% helium/21% oxygen) required to
elicit hypothermia (or the temperature at the cold limit,
TCL; after Saarela et
al., 1989
).
The objective of this study was to examine the interspecific relationship between cold tolerance (measured as TCL under a sliding helox cold exposure) and Msum in both summer and winter in a phylogenetically diverse sample of small birds. We used both standard and phylogenetically corrected methods to analyze the interspecific Msum/TCL relationship to determine whether phylogeny influenced any correlation between Msum and TCL. To our knowledge, this is the first study to directly examine, using relevant comparative techniques, whether an interspecific phenotypic correlation between cold tolerance and Msum exists for birds.
| Materials and methods |
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Measurement of cold tolerance and Msum
Standardized conditions for determining TCL must be
delineated to use cold tolerance data for comparative analyses. Swanson et al.
(1996
) suggested standard
methods for eliciting Msum in birds by sliding cold
exposure in helox that we adapted for measurement of TCL.
Using this method, we exposed an individual bird to a declining series of
temperatures in 79% helium/21% oxygen (helox), where temperature was decreased
by 3°C at 25 min after the initiation of cold exposure, and every 20 min
thereafter, until hypothermia was induced. We concluded that hypothermia had
occurred when oxygen consumption decreased steadily, without rebounding, over
several minutes, reaching levels lower than those recorded over the preceding
portion of the cold exposure test. To verify hypothermia, we removed birds
from the metabolic chamber and measured body temperature with a Cole-Parmer
Model 8500-40 Thermocouple Thermometer (Chicago, IL, USA) by inserting a
lubricated 20-gauge copper-constantan thermocouple into the cloaca to a depth
(approximately 1 cm) where further insertion did not alter the temperature
reading. We considered birds with body temperature
Tb<37°C as hypothermic, and birds were invariably
hypothermic when the conditions noted above had been met. We defined the helox
temperature at the beginning of this steady decline in oxygen consumption as
TCL. One further matter in the standardization of
TCL measurement involves the temperature at which the
sliding helox cold exposure is initiated. Because TCL is
affected by body mass, to keep thermogenic endurance roughly standardized
among species, cold exposure tests must begin at higher temperatures for
smaller birds.
Based upon previous studies using both sliding and static helox cold
exposure in both summer- and winter-acclimatized birds
(Dawson and Smith, 1986
;
Swanson, 1990a
,
1993
;
Cooper and Swanson, 1994
;
O'Connor, 1995a
;
Dutenhoffer and Swanson, 1996
;
Liknes and Swanson, 1996
;
Swanson et al., 1996
), we
initially measured TCL by sliding helox cold exposure for
nine species of summer-acclimatized passerines (Contopus virens, Tyrannus
tyrannus, Vireo gilvus, Dumetella carolinensis, Troglodytes aedon, Carduelis
tristis, Spizella pusilla, Dendroica petechia and Pheucticus
ludovicianus) ranging from 9.6 to 40.7 g mean body mass, and for five
species of winter-acclimated birds (Picoides pubescens, P. villosus, Sitta
carolinensis, Cardinalis cardinalis and Passer domesticus)
ranging from 21.8 to 62.6 g mean body mass. For TCL
measurements on these species, sliding helox cold exposure was initiated
either (1) at 6-8°C above temperatures producing hypothermia in a majority
of individuals within 1 h in previous studies using static cold exposure, (2)
at 6-8°C above TCL from previous studies using sliding
cold exposure, or (3) if cold tolerance had not previously been measured for
that species, from extrapolations based on body mass from previous studies on
other species. From these TCL data, we calculated mean
TCL for each of these species and generated an allometric
equation predicting TCL for both summer- and
winter-acclimatized birds:
![]() |
![]() |
where TCL is in °C and Mb is in g. For subsequent TCL experiments, sliding helox cold exposure was initiated at temperatures 6°C above the allometrically predicted TCL. The initial temperature was then modified for each species, as needed, so that hypothermia did not occur too rapidly (<45 min) or too slowly (>2 h) for comparative purposes.
In the current study, we measured TCL concurrently with
Msum determination on individual birds. We measured summit
metabolic rate by open-circuit respirometry using a sliding cold exposure in
helox (Swanson et al., 1996
).
Briefly, we placed birds into 1.9 l or 3.8 l paint cans (depending on body
size), with the inner surface painted flat black to provide emissivities near
1.0, which served as metabolic chambers. Mean effective volumes of these
chambers, calculated according to Bartholomew et al.
(1981
), were 1917 ml and 4688
ml for the 1.9 l and 3.8 l chambers, respectively. We achieved temperature
control within metabolic chambers by immersing them into a bath of water and
propylene glycol (Forma Scientific Model 2095; Marietta, OH, USA), which
regulated chamber temperature to ±0.5°C. Prior to immersion, we
flushed the chamber for at least 5 min with helox to replace air with helox.
We maintained flow rates of dry, CO2-free, helox at 1010-1030 ml
min-1 over the course of the experiments using a Cole-Parmer
Precision Rotameter (Model FM082-03ST; Chicago, IL, USA), previously
calibrated to ±1% accuracy. We measured fractional oxygen content in
excurrent gas leaving the chamber using an Ametek S-3A oxygen analyzer
(Pittsburgh, PA, USA). We recorded fractional oxygen content every 60 s over
the test period and computed oxygen consumption according to the instantaneous
equations of Bartholomew et al.
(1981
). We then calculated
consecutive 10 min means for oxygen consumption rates over the test period
(1-10, 2-11, etc.) and considered the highest 10 min mean, excluding the
initial 10 min of measurements), as Msum
(Dawson and Smith, 1986
). We
corrected all values for oxygen consumption to STPD and converted oxygen
consumption to metabolic rates (in W) by assuming an energy equivalent of 20.1
J ml-1 O2.
|
Data analyses
We analyzed the relationship between Msum and
TCL both by conventional statistical methods and by
phylogenetically independent contrasts
(Felsenstein, 1985
;
Garland et al., 1992
). For
conventional analyses, we performed least-squares regressions of
logMb vs logMsum and
logMb vs logTCL. We then
calculated residuals from these allometric equations and performed
least-squares regression of residuals of logTCL against
residuals of logMsum. While this approach controls for the
effects of mass on the Msum/TCL
relationship, it does not account for possible phylogenetic influence on the
relationship.
Consequently, we calculated phylogenetically independent contrasts (PIC)
for logMb, logMsum and
logTCL according to Garland et al.
(1992
,
1993
). Calculation of
phylogenetically independent contrasts requires knowledge of tree topology and
branch lengths, which we garnered from Sibley and Ahlquist
(1990
)
(Fig. 1). Most species for
which we measured Msum in this study either have branch
length data provided directly in the study of Sibley and Ahlquist
(1990
) or are closely related
to species that are listed, so that branch lengths can be determined. We used
arbitrary branch lengths of 1.0 in the summer analysis for divergences of
chipping (Spizella passerina) and field (S. pusilla)
sparrows and for Baltimore (Icterus galbula) and orchard (I.
spurious) orioles (based, respectively, on divergence distances within
Melospiza sparrows of 1.3 or less and a divergence distance of 1.2
for orioles and New World blackbirds;
Sibley and Ahlquist, 1990
). In
addition, we used a branch length of 2.8 for the Bell's-warbling vireo
divergence, because that is the divergence distance between congeneric
blue-headed and white-eyed vireos (Sibley
and Ahlquist, 1990
). In addition, analyses using PIC are robust to
actual branch length variation (Garland et
al., 1999
), so the few arbitrary branch lengths used in this study
are unlikely to influence PIC results. We initially standardized contrasts by
dividing by branch lengths, but absolute values of contrasts were potentially
correlated with their branch lengths, so branch lengths were log-transformed
after first increasing the scale of the entire phylogenetic tree by a factor
of 10. This reduced correlations to non-significant levels so that contrasts
were weighted equally in subsequent analyses. Standardized contrasts were
positivized on Mb according to Garland et al.
(1992
). We then performed
least-squares regression through the origin on positivized contrasts of
logMb vs logMsum and on
logMb vs logTCL. We
calculated residuals from logMsum and
logTCL PIC allometric regressions and performed
least-squares regression on residuals of logTCL contrasts
against residuals of logMsum contrasts to test for
phenotypic correlation independent of body mass and phylogeny.
To analyze phylogenetic diversity in the relationship between Msum and TCL, we calculated 95% confidence intervals around allometric regression lines for raw data and PIC regressions for Msum and TCL. We considered values for species (raw data) or for ancestral nodes (PIC) falling outside these confidence intervals as having high or low Msum or TCL (for allometric regressions).
| Results |
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![]() |
![]() |
Slopes of logMsum vs logMb
regressions did not differ significantly between seasons
(F1,60=4.63, P>0.05), but the winter intercept
was significantly higher than the summer intercept
(F1,29=27.83, P<0.001). Similarly,
logMb (in g) and logTCL (in °K)
were significantly negatively related in both summer and winter birds
(Fig. 2B). Regression equations
were:
![]() |
![]() |
Slopes of logTCL vs logMb regressions did not differ significantly between seasons (F1,60=1.44, P>0.05), but the winter intercept was significantly lower than the summer intercept (F1,29=87.99, P<0.001). Residuals of logMsum/logMb and logTCL/logMb regressions were significantly negatively related in both summer (R2=0.34, P=0.006) and winter (R2=0.40, P=0.037) (Fig. 3).
|
Phylogenetically independent contrast analysis
Least-squares regression through the origin of phylogenetically independent
contrasts of logMsum against logMb
yielded significant positive relationships for both summer and winter birds.
For summer birds, regression statistics were b=0.70,
R2=0.66, P<0.001. Regression statistics for
this relationship in winter were b=0.50, R2=0.73,
P=0.001. Regressions through the origin for logMb
and logTCL (°K) contrasts were significantly
negatively related in summer birds and showed a similar non-significant trend
for winter birds. Regression statistics for the summer equation were
b=-0.029, R2=0.29, P=0.012. Regression
statistics for the winter equation were b=-0.012,
R2=0.25, P=0.124. Residuals from allometric
equations for logMsum and logTCL
contrasts were significantly negatively correlated in both summer
(R2=0.47, P<0.001) and winter
(R2=0.40, P=0.049)
(Fig. 4).
|
Phylogenetic diversity
Species exhibiting high Msum in summer included downy
woodpecker, house wren, black-capped chickadee, house sparrow, American
goldfinch and field sparrow (Fig.
5A). Species with low Msum in summer were
eastern wood-pewee, Bell's vireo, gray catbird, white-breasted nuthatch,
orchard oriole and rose-breasted grosbeak. Those species with high or low
Msum also generally showed low or high
TCL, respectively. Exceptions included gray catbird and
rose-breasted grosbeak, which had low Msum but typical
TCL, house wren and black-capped chickadee, which had high
Msum but typical TCL, downy
woodpecker, which had high Msum but high
TCL, and house finch, which had typical
Msum but low TCL.
|
PIC analyses documented ancestral nodes showing high or low Msum or TCL (Fig. 5). For summer analyses, nodes with high Msum included the root node for the entire tree, the vireo node, the house sparrow-sister taxon node, the Spizella node, the warbler-oriole/cardinalid node, and the oriole node (Fig. 5A). Nodes with low Msum were the catbird node, the chickadee-nuthatch/wren node, the nuthatch-wren node, the Spizella-warbler/oriole/cardinalid node and the oriole-cardinalid node (Fig. 5B). Nodes showing high or low Msum also generally showed low or high TCL, respectively. Exceptions included the root node and the warbler-oriole/cardinalid node, which had high Msum but typical TCL, the oriole-cardinalid node, which had low Msum but typical TCL, and the woodpecker and nuthatch/wren/chickadee nodes, which had typical Msum but low TCL. The only winter node with high Msum was the house sparrow-finch/sparrow/cardinalid node, but this node showed typical TCL. The only winter node with low Msum was the nuthatch-chickadee node, which also showed high TCL. The nuthatch/chickadee-sister taxon and horned lark-sister taxon nodes both showed low TCL but typical Msum.
| Discussion |
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Such a correlation is also generally consistent with previous data on
seasonal acclimatization in small birds. A few birds exhibit seasonal changes
in cold tolerance without accompanying seasonal changes in thermogenic
capacity, and geographic variation in cold tolerance is not always associated
with corresponding variation in thermogenic capacity
(Dawson et al., 1983a
;
Swanson, 1993
;
Saarela et al., 1995
). Such
data have cast doubt on the generality of the correlation between thermogenic
capacity and cold tolerance. However, most species of small birds do show a
significant winter increment of thermogenic capacity that is associated with
substantial improvements in capacity to tolerate cold temperatures
(Hart, 1962
;
Swanson, 1990a
;
Cooper and Swanson, 1994
;
O'Connor, 1995a
;
Liknes and Swanson, 1996
;
Liknes et al., 2002
;
Cooper, 2002
;
Arens and Cooper, 2005a
).
Winter increments of thermogenic capacity documented in these studies range
from 16-55%. If thermogenic capacity and cold tolerance are generally elevated
in winter relative to summer in small birds, then regression equations of
logMsum on logMb should be elevated,
and regression equations of logTCL on
logMb should be lower, in winter compared to summer. Such
was indeed the case in this study, as slopes of these regressions did not
differ significantly between seasons, but intercepts were significantly higher
for Msum and significantly lower for
TCL in winter than in summer. In general, therefore,
winter birds had higher thermogenic capacity and tolerated colder temperatures
in helox than summer birds. For example, according to the regression equations
in this study, a 20 g bird would have a 28.2% higher Msum
and would require a helox temperature 6.8°C lower to induce hypothermia in
winter relative to summer. The seasonal temperature difference for hypothermia
induction in helox substantially underestimates the actual seasonal
temperature difference in air, as helox markedly increases thermal
conductivity relative to air in small birds
(Dawson and Smith, 1986
;
Swanson, 1993
;
Cooper, 2002
), so seasonal
differences in cold tolerance are quite marked for the species in this
study.
Thus, winter increment of thermogenic capacity appears to be a common
component of seasonal acclimatization in small birds. Taken together, data
demonstrating concomitant seasonal variation in cold tolerance and thermogenic
capacity and direct demonstration of correlations between cold tolerance and
thermogenic capacity, both within and among species, strongly suggest that
physiological adjustments promoting increased thermogenic capacity in small
birds also promote elevated cold tolerance. This suggests that cold tolerance
(i.e. thermogenic endurance) and thermogenic capacity are functionally linked,
potentially through variation in muscle mass or by adjustments of
mass-specific metabolic intensity or capacity to oxidize fuels, principally
fat (Dawson et al., 1983b
;
Marsh and Dawson, 1989
;
Swanson, in press). Such a link is consistent with the general vertebrate
pattern of coupled variation in endurance and aerobic capacity
(Bennett, 1991
).
Because metabolic rates (M) in endotherms can be defined by:
![]() |
where C is thermal conductance (a net measure of heat transfer
between the animal and the environment), Tb is body
temperature and Ta is ambient temperature, a link between
Msum and TCL is perhaps not
surprising. At temperatures eliciting maximum cold-induced metabolic rates in
birds, Msum and TCL can potentially be
substituted into the above equation, yielding, after rearrangement:
![]() |
which suggests that Msum and TCL
should be linked (e.g. Bozinovic and
Rosenmann, 1989
). However, two factors could influence this
purported linkage. First, variation in Msum is not the
only factor that influences TCL. Concurrent variation in
C or Tb could offset any variation in
Msum, such that Msum and
TCL might not be correlated. In essence, testing for a
correlation between Msum and TCL is
akin to testing for how much variation in TCL is explained
by variation in Msum, rather than by other factors that
affect C or Tb. Second, substituting
Msum and TCL into the above equation
assumes that TCL always occurs concurrently with
Msum, but this is often not the case, as the highest
metabolic rates (Msum) during cold exposure treatments,
such as those in this study, usually occur well before temperatures eliciting
hypothermia (Swanson, 2001
).
Thus, substituting Msum and TCL into
the equation describing metabolic rates in endotherms is probably not strictly
appropriate.
R2 values for regressions of residuals from allometric
equations for Msum and TCL ranged from
34-47% in this study, indicating that interspecific variation in thermogenic
capacity explained a substantial portion of the interspecific variation in
cold tolerance. However, substantial variation in cold tolerance still remains
unexplained, which suggests a role for other factors in affecting differences
in cold tolerance among species and seasons. Such factors could include
differences in insulation, control over thermal conductance, circulatory and
ventilatory differences (Swanson,
1990b
; Breuer et al.,
1995
; Arens and Cooper,
2005a
,b
),
or metabolic adjustments promoting shivering endurance without affecting
thermogenic capacity (Marsh and Dawson,
1982
; Yacoe and Dawson,
1983
; Marsh et al.,
1990
).
Because seasonal acclimatization in birds is largely a metabolic process,
with only a minor role played by seasonal changes in insulation
(Dawson et al., 1983b
;
Marsh and Dawson, 1989
;
Swanson, 1991a
), metabolic
adjustments should play a prominent role in explaining both seasonal and
interspecific variation in cold tolerance. Such metabolic adjustments could
include those affecting fuel mobilization and supply to shivering muscles, as
well as those promoting preferential use of lipid to fuel shivering
(Marsh and Dawson, 1982
;
Yacoe and Dawson, 1983
;
Marsh et al., 1990
;
Swanson, 1991b
;
O'Connor, 1995b
). These
adjustments would not necessarily be reflected by increases in thermogenic
capacity, but could increase cold tolerance by elevating the percentage of
thermogenic capacity that could be sustained for prolonged periods. This model
for seasonal variation in cold tolerance was posited by Marsh and Dawson
(1989
), largely from studies
on American goldfinches and house finches. Liknes et al.
(2002
) termed this model the
variable fraction model, because the model contends that it is the fraction of
thermogenic capacity that is sustainable which varies seasonally, rather than
the thermogenic capacity. In contrast to this model is the variable maximum
model (Liknes et al., 2002
),
which posits that it is thermogenic capacity that varies seasonally. The
winter increment of thermogenic capacity, in turn, increases thermogenic
endurance in the cold, because as thermogenic capacity increases, the absolute
rate of sustainable heat production also increases, even if the fraction of
thermogenic capacity that is sustainable remains seasonally constant. Because
the data in this study indicate a winter increment of thermogenic capacity and
directly document a correlation between thermogenic capacity and cold
tolerance in small birds, they are consistent with the variable maximum model.
However, it is important to note that metabolic adjustments promoting
maintenance of a higher sustained fraction of thermogenic capacity could
further improve cold tolerance, and therefore might help account for some of
the unexplained variation in cold tolerance in this study.
Some interesting general trends emerged from analyses of phylogenetic
diversity in the relationship between Msum and
TCL. For summer analyses, the root node had high
Msum, but typical TCL based on
allometric predictions, whereas in winter the root node was typical for both
parameters. The summer data suggest that ancestral species had high
thermogenic capacity, but were relatively poorly insulated, resulting in
relatively poor cold tolerance for their metabolic abilities. However, in
winter, where taxa not resident in cold climates were absent from the
analyses, the root node was typical for both Msum and
TCL, suggesting that it is taxa not resident in cold
climates that were driving the uncoupling of Msum and
TCL from summer analyses. Another factor likely influences
this uncoupling, however, and that is the absence of a winter increase in
Msum in downy woodpeckers in this study. Because downy
woodpeckers had high Msum in summer and low
Msum in winter, and woodpeckers were one of the sister
taxa at this node, the nodal values were likely influenced by the absence of a
seasonal difference in Msum in this species. The lack of a
seasonal difference in Msum in downy woodpeckers differs
from that previously documented for this species by Liknes and Swanson
(1996
), where
Msum in winter was 52% greater than that in summer. The
reason for the difference between these two studies is unknown, but may
involve differences in winter weather among years, which can impact metabolic
rates in birds (Swanson and Olmstead,
1999
).
Another noteworthy finding from summer analyses was that high Msum and low TCL, as well as low Msum and high TCL, occurred in taxa composed solely of migrants, as well as taxa with members wintering in cold climates. This suggests that physiological capacities for heat production or cold tolerance are not the sole determinant of wintering strategy within a taxon. Finally, although deviations from allometric predictions for Msum and TCL were usually coupled for species and for ancestral nodes, this was not always the case. This again suggests that while thermogenic capacity is a prominent factor influencing cold tolerance, there is still room for factors other than thermogenic capacity in establishing differences in cold tolerance among species and seasons.
| Acknowledgments |
|---|
| Footnotes |
|---|
Present address: Department of Biology, Augustana College, 2001 S. Summit
Avenue, Sioux Falls, SD 57197, USA | References |
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