Allometric scaling of flight energetics in Panamanian orchid bees: a comparative phylogenetic approach
,*
1 Department of Zoology, University of British Columbia, Vancouver, BC,
Canada, V6T 1Z4
2 Department of Ecology, Evolution and Marine Biology, University of
California Santa Barbara, Santa Barbara, CA 93106-9610, USA
3 Smithsonian Tropical Research Institute, Balboa, Republic of
Panama
* Author for correspondence (e-mail: darveau{at}zoology.ubc.ca)
Accepted 6 July 2005
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Summary |
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Key words: metabolic rate, evolution, allometry, wingbeat frequency, wing loading, phylogenetically independent contrasts, orchid bee
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Introduction |
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Phylogenetic information is often incorporated into analysis of continuous
traits by using a hypothesized tree topology and associated branch lengths
(Garland et al., 1992). In
this study, we present a new phylogenetic hypothesis for orchid bees based on
the cytochrome b gene (cyt b) partial sequences, and use it
to analyze metabolic rate evolution. Using phylogenetically independent
contrasts (PIC), we analyze the correlated evolution of body mass, wing
morphology, wingbeat frequency, and the energetic cost of hovering flight.
This range of data provides a new framework for the understanding of
evolutionary relationships between species size and metabolism.
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Materials and methods |
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Molecular phylogeny
To assess the phylogenetic relatedness of the various species used, we
generated hypothetical phylogenies based on partial sequences for the
mitochondrial gene cyt b. We extracted total DNA from individuals of
32 species, mainly from the thorax (sometimes including legs), using the
DNeasy DNA extraction kit (Qiagen, Mississauga, ON, Canada), following the
manufacturer's recommended homogenization procedure for insects.
The amplification procedure used was modified from Koulianos and
Schmid-Hempel (2000). Using
their cyt b primers, we amplified a 716 bp fragment. The conditions
that yielded the highest amplification success for most species were as
follows: denaturation at 94°C for an initial 2 min and then 40 cycles of 1
min denaturation at 94°C, annealing at 43°C for 1 min, extension at
72°C for 1 min, followed by a 5 min final extension (Perkin-Elmer DNA
Thermal Cycler, Woodbridge, ON, Canada); Taq polymerase 2.5 U
(Invitrogen, Burlington, ON, Canada), MgCl2 1.5 mmol, dNTP 0.2
mmol, primers 0.5 µmol l-1. For some species, the yield of
amplification product was low. This required slight modification of time,
temperature, MgCl2 and Taq concentrations. DNA product was
purified using Quiaquick purification columns (Qiagen). Sequencing was
performed by the University of British Columbia Nucleic Acid and Protein
Sequencing Unit using an ABI 377 automated sequencer. The primers used for
sequencing were the same as those used for amplification. In most cases, one
individual was sufficient to provide a sequence.
The cyt b sequences (650 bp) obtained for 32 species were
aligned using ClustalX. Sequences were also obtained from GenBank for
Eulaema meriana (GenBank accession number AF181614), El.
bombiformis (AF002728), Eufriesea caerulescens (AF181613), in
addition to the selected outgroups: Xylocopa virginica (AF181618),
Apis mellifera (NC_001566), Bombus hyperboreus (AF066968),
Trigona hypogea (AF181617) and Melipona bicolor (NC_004529).
Trees were rooted with Xylocopa virginica, which is hypothesized to
be a sister clade (Ascher and Danforth,
2001
).
Aligned sequences were imported into PHYML for maximum likelihood analysis.
Several sequence divergence models were executed, and a general time reversal
with gamma distribution was selected based on log-likelihood score. The
support for the nodes was obtained with 1000 bootstrap replicates using PHYML
and SEQBOOT and CONSENS from PHYLIP 3.57c
(Felsenstein, 1995).
Additional analyses were performed with the neighbor-joining distance method
using MEGA version 2.1 (Kumar et al.,
2001
), and Bayesian analysis using MrBayes 3.0
(Huelsenbeck and Ronquist,
2001
).
Respirometry and wingbeat frequency measurements
Rates of CO2 production
(CO2 values)
were measured as described in the accompanying paper
(Suarez et al., 2005
) using a
FOX flow-through field respirometry system equipped with a Sable Systems
(Henderson, NV, USA) CA-2A CO2 analyzer. Smaller species, up to 400
mg in body mass, were flown in a 0.5 l flask with sidearm, while a similar
flask of 1 l capacity was used for larger species. Air was drawn into the
flasks through perforated rubber stoppers and out through the sidearms through
Tygon tubing at a rate of 1.5 l min-1. Orchid bees, like honeybees
(Crabtree and Newsholme, 1972
;
Rothe and Nachtigall, 1989
)
fuel flight exclusively by carbohydrate
(Suarez et al., 2005
).
Therefore, the
CO2 values
measured are equivalent to rates of O2 consumption
(
O2), i.e. RQ=1
(Suarez et al., 2005
). Data
acquisition and analysis were performed using Datacan (Sable Systems).
Wingbeat frequency measurements were performed simultaneously using an optical flight detector (Qubit systems, Kingston, ON, Canada), positioned beneath the respirometry chambers. The instrument was linked to a portable computer and the signal was acquired and analyzed using Trex (Qubit systems). The wingbeat of an insect performing hovering flight in the chamber was detected by the photocell. Intervals of 2 s were analyzed for fundamental frequency and data for each individual were averaged for the initial 30 s of flight.
Wing morphology
The individuals were frozen on dry ice, stored at -80°C, and brought to
the laboratory for morphological measurements. From each individual, one pair
of wings was removed and flattened between microscope slides. A digital image
of the wings on a 1 cm grid background was taken for image analysis using
Scion image.
Data analysis
All data are presented as species means ± S.D.
(standard deviations) of individual measurements. However, both individual
data and species means were analyzed. The effect of body mass
Mb on the different characters was tested using the
least-squares linear regression performed on log-transformed data to obtain
the power equation Y=aMbb. Further
analyses of wingbeat frequencies and mass-specific metabolic rates were
performed using stepwise regressions to test the relative importance of body
mass, forewing length, total wing area and wing loading. In addition, we
corrected for body mass covariation using an analysis of residuals.
Analysis of phylogenetically independent contrasts was conducted using the
PDAP (Midford et al., 2003)
module in Mesquite (Maddison and Maddison,
2004
). Standardized independent contrasts were obtained from the
log-transformed character data, and presented using the maximum likelihood
tree obtained from cyt b sequence information
(Fig. 1). This phylogenetic
tree was pruned to include only the 12 species from which a complete set of
flight data was obtained (Fig.
2). We also performed all analyses using the hypothesized trees
obtained from our other phylogenetic analyses (neighbor-joining, Bayesian,
maximum parsimony). The results of these analyses were qualitatively the same
(data not shown). To further evaluate phylogenetic tree topology and branch
length uncertainty (see Garland et al.,
1992
,
1993
;
Martins and Housworth, 2002
),
we implemented our analysis of character data using 10 000 trees generated
from a Bayesian analysis and reported the correlation coefficient frequency
distribution of standardized independent contrasts using Mesquite. The same
analysis performed with 10 000 simulated trees yielded the same results (not
shown). We analyzed the relationship among independent contrasts with the same
series of tests we used for the conventional analysis. We first tested body
mass scaling effects on the different characters, using least-squares linear
regression through the origin performed to analyze standardized contrasts
(Garland et al., 1992
). We
then carried out stepwise regression through the origin to analyze the
independent contrasts of wingbeat frequency and mass-specific metabolic rate,
to test for the effects of body mass, forewing length, total wing area and
wing loading. Finally we statistically controlled for body mass using the
residuals obtained from the independent contrasts body mass regressions, and
plotted the residuals and tested for regression through the origin
(Garland et al., 1992
). All
analyses were performed first using a model of gradual evolution, where
characters usually experience greater changes along longer branches
(Garland et al., 1993
). We
ensured that branch lengths adequately standardized the contrasts by plotting
the absolute value of standardized independent contrasts and their standard
deviation (Garland et al.,
1992
). The raw branch lengths obtained from cyt b genetic
distances were used, but several branch length transformations (Grafen, Pagel,
Nee, logarithmic) were also tested and yielded the same results (not shown). A
speciational model of evolution, where changes occur with a speciation event,
was simulated by setting all branch lengths to 1
(Garland et al., 1993
).
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Results |
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Although ten sequences were incomplete and seven sequences taken from
GenBank were partially overlapping (550 bp), the aligned sequences were
sufficient to yield a hypothetical phylogeny
(Fig. 1). This maximum
likelihood tree was obtained using the general time reversal (GTR) model with
gamma distribution (=0.501), with node support indicated by the
bootstrap value. In the Euglossa, nodes were supported near the crown
species, but deeper nodes in that clade were poorly resolved. The genera
Eulaema and Eufriesea appeared to form a clade which was not
supported with high bootstrap values. Nonetheless, the topology obtained with
neighbor-joining and Bayesian methods (not shown) always grouped the two
genera. The cyt b information places Exaerete as the sister
genus of the Eulaema-Eufriesea group. Finally, the
Euglossa genus was resolved as sister of the other genera.
Alternative methods of phylogenetic inference and genetic distance methods
yielded similar topologies with nodes with bootstrap values greater than 50%
generally conserved (results not shown).
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Analyses of body mass relationship with wingbeat frequency performed using PIC are presented in Table 1 and Fig. 5. When the hypothesized phylogeny in Fig. 1 was applied, the body mass effect remained highly significant under both gradual and speciational models of character evolution (Table 1). The exponent was similar to that obtained from conventional analysis, with -0.30 (CL: -0.18, -0.43) for gradual and -0.30 (CL: -0.17, -0.43) for speciational. The uncertainty of the hypothesized phylogeny was tested by analysis of the contrasts between body mass and wingbeat frequency using 10 000 different trees obtained by Bayesian analysis. The resulting frequency distribution indicated that wingbeat frequency and body mass evolution were closely correlated (Fig. 5). The average correlation coefficient of the distribution was 0.86, and the values obtained from the cyt b tree in Fig. 1 were 0.87 and 0.85 for gradual and speciational evolution, respectively (Table 1 and Fig. 5A).
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Incorporating phylogenetic information (Fig. 1) into the analysis confirmed the relationships presented above, in which mass-specific metabolic rate was accounted for chiefly by wingbeat frequency, followed by wing length and area, and then by body mass. Analyzing the effect of body mass on mass-specific metabolic rate yielded similar exponents for gradual (-0.27 CL: -0.11, -0.42) and speciational evolution (-0.28 CL: -0.14, -0.43). A test for phylogenetic uncertainty (Fig. 5B), revealed a significant average correlation coefficient, 0.76, while the values obtained using the phylogeny in Fig. 1 were 0.78 and 0.80 for gradual and speciational evolution, respectively (Table 2 and Fig. 5B).
Morphology, wingbeat frequency and metabolic rate
To analyze the interrelationships between the body mass, wing morphology,
wingbeat frequency and metabolic rate, we first performed stepwise
regressions. For wingbeat frequency, wing area was introduced in the
regression model, and much of the remaining variation was explained by wing
loading. Moreover, using any of the three variables associated with size (body
mass, wing length, wing area), and wing loading, the coefficient of
determination of the model was 98%. Stepwise regression analysis of
mass-specific metabolic rate introduced wingbeat frequency alone in the model.
Performing these analyses using PIC produced the same qualitative results. In
addition, the wingbeat frequency and metabolic rate relationship was analyzed
for uncertainty (Fig. 5C), and
a high average correlation coefficient was observed (r=0.90),
comparable to those observed with our cyt b phylogeny (0.89 and 0.95
for gradual and speciational evolution).
The functional relationships among the variables were further analyzed by examining residuals obtained from the body mass relationship, i.e. as body mass corrected variation. The wingbeat frequency residual variation was largely explained (r2=0.86) by residual variation in wing loading (Fig. 7A). The residuals obtained for wing length and wing area also had strong correlations (not shown), indicating that a species of a given mass with longer wings or wings of greater area had lower wingbeat frequency; i.e. there was positive correlation between wing loading and wingbeat frequency. The same analyses performed using residuals obtained from the contrast values for gradual and speciational evolution also yielded strong positive correlations between wingbeat frequency and wing loading (Fig. 7B).
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Discussion |
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Prior to our work, there have been no phylogenies available at the species
level for orchid bees (but see Dick et al.,
2004). Our hypothesized phylogeny
(Fig. 1), which includes 37
species in four genera and several subgenera, shows groups almost entirely
consistent with the higher taxonomic scheme (subgenera and genera) discussed
by Dressler (1978
), and is at
least compatible with the phylogeny suggested by Cameron
(2004
). Nodes near the tip of
the tree are supported by high bootstrap values, while the deeper nodes are
generally characterized by low values (Fig.
1). For example, the species groups classified by Dressler
(1978
) that are found in
subgenus Euglossa are sometimes separated in our phylogeny,
indicating that they may not derive from a common ancestor (e.g.
Euglossa VIII, X, XII in Fig.
1). Other recent work with a number of widespread species and a
CO1 marker (Dick et al., 2004
)
has shown reticulate evolution of species pairs, and species associations not
in agreement with some previous classifications. Thus, we use our hypothesized
tree with caution, because the tree topology of the orchid bees is
unstable.
Body mass, wing morphology and wingbeat frequency
Our analysis of flight performance and its determinants established the
linkages between wing morphology and wingbeat frequency during hovering
flight. The variation in wingbeat frequency among species was explained well
by their body mass (Fig. 4).
This allometric relationship and the scaling exponent of -0.31
(Fig. 4) are consistent with
results obtained previously by Casey et al.
(1985) and, more recently, with
results obtained in load-lifting experiments
(Dillon and Dudley, 2004
). The
effect of size on wingbeat frequency can be understood in terms of the
resonance properties of the flight apparatus. For a mechanically resonant
system such as an asynchronous muscle, the oscillation frequency is inversely
proportional to the inertial load on the system, which corresponds to the mass
distribution along the wing length
(Dudley, 2000
). Indeed,
reducing inertial load on asynchronous flight muscle by cutting short the
wings increases the frequency, as expected for a resonant system
(Sotavalta, 1952
). Thus,
although body mass and wingbeat frequency are correlated, it is the wing size
(length and area) that more directly influences wingbeat frequency. Such a
conclusion is supported by our data showing that wing length and wing area
were related to wingbeat frequency with greater coefficients of determination
than was body mass (Table
1).
The relationship between wing loading and wingbeat frequency was both
negative and weak (Table 1).
Body mass evidently had differential effects on these two variables: a
negative scaling effect on frequency but a positive one for wing loading.
Together, these result in the weak negative correlation between wing loading
and wingbeat frequency. Byrne et al.
(1988) investigated the
relationship between body mass, wing loading and wingbeat frequency in insects
ranging from 3.3x10-5 to 2.8 g
(Fig. 10). They found that
wing loading and wingbeat frequency are positively correlated when data are
analyzed within relatively small ranges of body size. However, when data for
the entire size range are analyzed, wingbeat frequency and wing loading are no
longer correlated. In the present work, we account for the confounding effects
of body mass by statistical removal of its effect on wing loading and wingbeat
frequency. This procedure resulted in a strong positive correlation between
the two variables (Fig.
7A).Reanalysis of the data of Byrne et al.
(1988
) along with our
euglossine data reveals the same pattern, with a high correlation coefficient
(r=0.89) for the relationship between residuals
(Fig. 10D). We conclude that
in orchid bees and possibly among flying insects in general, wingbeat
frequency is strongly related to wing loading, after controlling for
covariation in body mass.
|
Dudley (1995) studied
hovering flight kinematics in three species of orchid bee (Eg. dissimula,
Eg. imperialis and El. meriana) that flew in hypo-dense mixtures
of helium and oxygen (heliox). Wingbeat amplitude increased from about
105° in normal air to 140° when flying in heliox. Dillon and Dudley
(2004
) investigated maximal
load-lifting flight capacity in 11 species of orchid bees. They found that, in
flight, orchid bees could sustain about twice their own body mass, and that
their load lifting capacity scaled isometrically. While lifting maximum loads,
wingbeat frequencies scale allometrically with an exponent similar to that
during normal hovering. The wing stroke amplitudes, however, increase to
approximately 140° in all species. Thus, interspecifically, stroke
amplitude is conserved during normal hovering in ambient air while,
intraspecifically, flight power output in response to various imposed loads
can be modulated through changes in stroke amplitude. As the bees in the
present study were induced to hover in ambient air without added loads, it is
reasonable to assume there was a constant stroke amplitude across species.
PIC and correlated evolution
Using PIC analysis allowed incorporation of phylogenetic information to
study mechanistic and statistical relationships in the data. The utility of
such an approach can be appreciated by considering how markedly the body mass
of species and correlated characters were influenced by phylogenetic group
(Fig. 2). An obvious weakness
of this approach is its dependence on correctness of the proposed phylogeny.
It has been shown that accounting for tree topology and branch length
uncertainty can provide some level of confidence in the interpretation of the
PIC results (Garland et al.,
1993; Martins,
1996
; Miles and Dunham,
1993
; Housworth and Martins,
2001
). The use of Mesquite allowed us to assess the sensitivity of
the results to variation in tree topology and branch lengths. By testing the
correlated evolution of characters using 10 000 phylogenies obtained from a
Bayesian analysis, including many unlikely scenarios, we show
(Fig. 5) that the character
data are strongly correlated within the vast majority of phylogenetic
relationships.
Concluding remarks
We have shown that the effect of body mass on metabolic rate during
hovering flight in orchid bees can be understood in terms of the scaling of
wing kinematics and morphometric parameters. The body mass effect on
metabolism during flight in this lineage occurs through the scaling of wing
form and wing loading, which in turn determine the scaling of wingbeat
frequency and, therefore, metabolic rate. Such results illustrate the linked
relationship of form and function in relation to locomotion, as well as the
importance of incorporating an integrative approach in studies of whole-animal
metabolic rate scaling.
Recently, models have been proposed to explain the allometric scaling of
metabolism based on the assumption that metabolic rates are limited by supply
rates via branching (Banavar et al., 2002) or fractal-like
(West et al., 1999)
distribution systems. In honey bees, flight metabolism does not appear to be
limited by O2 supply through the tracheal system which, apparently,
possesses considerable excess capacity
(Harrison et al., 2001
;
Joos et al., 1997
). Although
data in this regard are unavailable for orchid bees, our findings suggest that
wing form and kinematics, rather than merely supply limitations, are the main
determinants of flight metabolic rate and its allometric scaling.
During hovering flight in bees, >90% of the oxygen consumed is accounted
for by oxidative metabolism in the flight muscles. Within the flight muscles,
most of the ATP hydrolysis that occurs is due to actomyosin ATPase activity,
which is activated during high rates of cross-bridge cycling. It would be of
interest to examine the biochemical correlates of metabolic rate scaling in
orchid bees, a further dimension in the evolution of form and function in this
interesting clade (see accompanying paper,
Darveau et al., 2005).
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Acknowledgments |
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Footnotes |
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