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First published online December 14, 2007
Journal of Experimental Biology 211, 86-91 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.012211
Morphological and mechanical determinants of bite force in bats: do muscles matter?
1 Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610
Antwerpen, Belgium
2 Centro de Biodiversidad y Genética, Universidad Mayor de San Simon,
Cochabamba, Bolivia
* Author for correspondence (e-mail: anthony.herrel{at}ua.ac.be)
Accepted 24 October 2007
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: modeling, bite force, bats, muscle, mechanics
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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;
Huston, 1994;
Simmons, 2005) including
species that have specialized for eating fruits, insects, other vertebrates,
nectar and even blood (Freeman,
2000). Accordingly, the cranial structure in bats is unusually
variable among mammals and is thought to reflect specializations for feeding
(Freeman, 2000;
Van Cackenberghe et al., 2002)
and echolocation (Pedersen,
2000). However, recent analyses of cranial structure, feeding
behavior and bite force across a wide range of bats suggest that correlations
between morphology and performance (i.e. bite force) and/or ecology are not as
clearcut as previously thought (Aguirre et
al., 2002; Van Cakenberghe et
al., 2002; Dumont,
2004; Dumont and O'Neil,
2004). For example, most of the variation in bite force across a
wide range of phyllostomid bats was explained by differences in body size
rather than specific cranial traits
(Aguirre et al., 2002). This
suggests that the evolution towards high bite force capacity and dietary
diversity has gone hand in hand with the evolution of large body size, or
alternatively, that functionally relevant traits associated with the jaw
musculature (rather than cranial shape per se) are the principal
determinants of bite force capacity in bats.
Remarkably little is know about the muscular traits responsible for
generating bite forces in bats. Despite very good descriptive work on the
morphology of cranial muscles (McAllister,
1872; Wille, 1954;
Storch, 1968;
Czarnecki and Kallen, 1980)
little attention has been devoted to the functionally relevant components of
the cranial system and the jaw musculature such as muscle mass, muscle and
fiber orientation, fiber length and physiological cross sectional area (but
see De Gueldre and De Vree,
1990). Consequently, it is currently not known how variation in
cranial morphology in general, and muscle morphology in particular, is
translated into differential bite performance across species. Moreover, the
functional traits and jaw closer muscle groups determining bite force capacity
in bats are currently unknown. In mammals in general, however, it has been
shown that different muscle groups are important for animals that have to
generate bite force at relatively small gape angles (e.g. ungulates)
versus those that need high bite performance at large gapes (i.e.
carnivores) (see Turnbull,
1970). More specifically, herbivores such as ruminants appear to
invest most of their jaw muscle mass in the musculus masseter as this allows
them to generate high bite forces at these low gape angles. Carnivores, on the
other hand, have a relatively larger m. temporalis giving them a performance
advantage at large gapes. In accordance, previous data for bats suggest that
the m. temporalis complex is relatively larger in fruit eating bats such as
Pteropus that eat large fruits than in species consuming smaller food
items (De Gueldre and De Vree,
1990).
Given the importance of the m. temporalis in biting at large gape angles,
and the fact that harder prey are also larger on average and vice
versa (e.g. Aguirre et al.,
2003), one would expect the evolution towards high bite force to
be associated predominantly with an increase in the cross sectional area of
the m. temporalis (Turnbull,
1970). Frugivores, however, might benefit from more powerful m.
masseter, allowing them to extensively masticate their food and thus allow
them to separate the indigestible fibrous matter from the nutritious juice
(Dumont, 2003). Moreover,
increased muscle size and force output may allow frugivorous bats to
significantly reduce the time and energy spent processing their food or may
allow them to incorporate harder fruits into the diet
(Dumont, 1999;
Aguirre et al., 2003;
Dumont, 2003;
Dumont and O'Neil, 2004). As
it can be assumed that high bite forces are especially beneficial for bats
feeding on hard or large prey, species licking up nectar or blood should show
a reduced jaw closer muscle mass.
Assuming that bite force is indeed ecologically relevant, selection could
operate on different components of the jaw system. The force generating
capacity of a muscle is determined by its cross sectional area which, in turn,
is a function of the mass of the muscle, the length of the muscle fibers and
the pennation angle. Additionally, the orientation and position of the muscle
relative to the jaw joint will affect the moment arm of the muscle and thus
also the bite force generated. Given the strong mass constraints for flying
animals, one could expect that morphological changes in the system that allow
increased force output without an increase in mass would be selected for.
Additionally, the suggested trade-off between chewing rate and force
generating capacity (i.e. a force–velocity trade-off) may also constrain
the evolution of large jaw adductors. Thus we predict that the evolution of
high bite force capacity should be associated with changes in the orientation
of the muscles and a reduction in fiber length (associated with increased
pennation) rather than with increases in the mass of the cranial muscles
themselves. Selection for ingestion and biting of large food items, may
alternatively constrain fiber length (large mandibular excursions may induce
excessive stretch in the jaw adductors if fiber lengths are short, driving
muscle to operate away from its plateau on the length–tension curve)
(see Gans and De Vree, 1987)
thus leading to a relative decrease in bite strength. Alternatively, increases
in overall body size, which will result in a relatively rapid increase in bite
forces because of the differential scaling of cross sectional area relative to
cranial length, may be selected for as suggested previously
(Aguirre et al., 2002).
We have tested which components of the jaw adductor system are the best
predictors of bite force for a wide diversity of species. To do so, we use a
static bite force model that is critically tested using in vivo bite
force data of an independent sample of the same species
(Aguirre et al., 2002;
Dumont and Herrel, 2003). To
evaluate the importance of body size on bite force capacity we assessed
scaling relationships of morphological traits and bite force with cranial
length.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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). In addition,
three old-world species (Pteropus giganteus Brünnich 1782;
Eidolon helvum Kerr 1792; and Plecotus auritus Linnaeus
1758) were included in the analysis.
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Morphology
Specimens were measured using digital calipers (forearm length and skull
length; ±0.01 mm Mitutoyo CD-15B) and cranial muscles (m. masseter
superficialis, m. masseter profundus, m. zygomaticomandibularis superficialis,
m. zygomaticomandibularis profundus, m. temporalis superficialis, m.
temporalis medius, m. temporalis profundus, m. temporalis pars
suprazygomatica, m. pterygoideus and the m. digastricus; see
Fig. 1A) were removed under a
binocular microscope (M5 Wild, Wild Heerbrugg, Gais, Switzerland). Photographs
were taken from all stages of the dissection in lateral and dorsal view.
Muscles were removed on both sides and transferred to labeled vials containing
a 70% aqueous ethanol solution. Muscles were blotted dry and weighed to the
nearest 0.01 mg using a microbalance (Mettler Toledo MT5; Mettler-Toledo,
Inc., Columbus, OH, USA). Next, muscles were transferred to a 30% aqueous
nitric acid solution and left for 20–24 h after which the solution was
replaced by a 50% aqueous glycerin solution. Individual fibers were teased
apart using blunt-tipped glass needles and 15 fibers were selected randomly
and drawn using a binocular microscope with attached camera lucida (MT5 Wild).
Drawings were scanned and fiber lengths determined using ImageJ V1.31
software.
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Bite model
The model was identical to that previously described
(Cleuren et al., 1995;
Herrel et al., 1998a;
Herrel et al., 1998b) and
relies on the computation of the static force equilibrium. As input for the
model, the three-dimensional coordinates of origin and insertion and the
physiological cross sectional area of the jaw muscles are needed.
Additionally, the three-dimensional coordinates of the point of application of
the bite force and the center of rotation are needed
(Fig. 1B). These were
determined on lateral and dorsal pictures taken during the dissection. For
muscle bundles with relatively broad areas of origin and insertion, the
centroid of the insertion area was used. Physiological cross sectional areas
were calculated based on the mass of the muscles, a density of 1.06 g
cm–3 (Mendez and Keys,
1960), and the average fiber length for each muscle bundle. Since
complex pennate muscles were separated into their component parts no
correction for pennation was included. Cross sectional areas were scaled using
a conservative muscle stress estimate of 25 N cm–1
(Herzog, 1994).
For comparative purposes simulations were run at a gape angle of 20 degrees
and with all jaw closer muscles set maximally active for all individuals.
Published electromyographic data suggest that all jaw closer muscles are
indeed maximally or near maximally recruited during biting on hard or tough
foods (Kallen and Gans, 1972;
De Gueldre and De Vree, 1988).
Note, however, that maximal activation does not necessarily imply force
generation. Bite forces were calculated for a range of orientations of the
food reaction forces and at two different bite points (incisor and last
molar). Since results were similar for the two bite points (note, however,
that absolute forces differ for bites at different locations) (see also
Dumont and Herrel, 2003) we
report only those for a bite point at the incisor. Model output consists of
the magnitude of the bite forces and joint forces and the orientation of the
joint forces at any given orientation of the food reaction forces.
Analyses
For species where multiple individuals were available, species means were
calculated for all morphological traits. For the morphological data, muscles
from the different muscle complexes were grouped and means were calculated to
improve statistical power. Thus, in our regression models the m. masseter
superficialis, m. masseter profundus, m. zygomaticomandibularis superficialis,
and the m. zygomaticomandibularis profundus were grouped; the m. temporalis
superficialis, m. temporalis medius, m. temporalis profundus, m. temporalis
pars suprazygomatica were grouped, and the lateral and medial m. pterygoideus
were also grouped. Bite and joint forces were calculated based on individual
input data, after which they were averaged to obtain a species mean. All data
were log10-transformed before analyses. First, the scaling of all
morphological and functional traits with cranial length was investigated using
regression analyses. To test whether slopes differed from those predicted by
geometric similarity models t-tests were used. Next, calculated bite
forces were correlated with in vivo bite forces measured for the same
species to test the accuracy of the model output where possible (15 out of 16
included in our analysis). Finally, stepwise multiple regression models were
run with in vivo bite force as the dependent and all morphological
traits as independent variables using both raw data and independent
contrasts.
As species share their evolutionary history, they cannot be considered
independent data points (Felsenstein,
1985; Harvey and Pagel,
1991). Independent contrasts were calculated for all traits using
a tree obtained by pruning an existing super tree for bats
(Jones et al., 2002) (see
Fig. 2). All branch lengths
were set to unity since no data are available on divergence times for all
species included in the analysis [see Diaz Uriarte and Garland
(Diaz Uriarte and Garland,
1998) for the validity of this procedure]. Contrasts were
standardized by dividing by the square root of the sum of the branch lengths,
and used as input for regression analyses forced through the origin
(Garland et al., 1999). We did
not use phylogenetically informed analyses to test scaling predictions as
regression slopes through the origin represent the evolutionary covariation in
traits rather than functional covariation.
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|
RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Model versus in vivo bite forces
Calculated bite forces are a good predictor of in vivo bite force
data collected for an independent sample of the same species (r=0.87;
P<0.01). The slope of the regression between calculated and
measured bite force data (Fig.
4) is not significantly different from 1.0 (slope=1.25,
t=1.25, P=0.23). Thus our model accurately predicts bite
force capacity across a wide range of species with different morphologies and
phylogenetic histories. The correlation is not merely a consequence of body
size as a correlation between residual calculated bite force and residual
in vivo bite force was highly significant (r=0.67;
P=0.006).
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Functional determinants of bite force
A multiple regression with calculated bite forces at bite point 1 (food
reaction force orientation of 90°, gape angle of 20°) as dependent
variable and all morphological traits as independents retained a significant
model with skull length (β=0.75), residual m. temporalis mass
(β=0.62), m. temporalis fiber length (β=–0.21) and m. masseter
mass (β=0.16) as best predictors (r2=0.93;
P<0.01). When taking into account the phylogenetic relationships
among species a significant model with the independent contrasts of residual
m. temporalis mass (β=0.89), the contrasts of skull length (β=0.63)
and the contrasts of m. temporalis fiber length (β=–0.32) is
retained (r2=0.96; P<0.01).
In vivo bite forces for the same set of species are best explained by residual m. temporalis mass and skull length. These results are identical when using traditional (r2=0.86; P<0.01; skull length: β=0.77, temporalis mass: β=0.51) or phylogenetically informed (r2=0.90; P<0.01; skull length: β=0.94, temporalis mass: β=0.33) analyses.
To test the role of muscle mass versus muscle fiber length in generating bite forces, we ran two regression models: one with only the residual muscle masses and a second one with both muscle masses and fiber lengths. Our first model explained 63% of the total variation in residual bite force; our second model explained an additional 13% of the variation in bite force in our data set.
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Although comparative data sets on scaling of functionally relevant muscle
properties, such as physiological cross sectional area, moment arms and fiber
lengths, are limited, the available data suggests that bats may be divergent
from other vertebrates. In fish (Herrel et
al., 2005), rodents
(Druzinsky, 1993), primates
(Anapol et al., in press) and
humans (Weijs and Hillen,
1985) the physiological cross sectional area of the jaw muscles
scales with strong positive allometry. In accordance, analyses of bite force
scaling in different vertebrate groups also suggest strong positive allometry
of the force generation capacity of the jaw system (e.g.
Herrel et al., 2002;
Herrel and O'Reilly, 2006). A
comparison of the data presented here with those provided by Herrel and
coworkers (Herrel et al.,
2005) on the scaling of fish muscles suggests that the difference
in scaling in muscle cross sectional area between the two groups is largely
due to the strong positive allometry of muscle mass in fish. As an exception,
the scaling relationships of physiological cross sectional area and muscle
fiber length were similar in bats and strepsirrhine primates
(Perry and Hartstone-Rose,
2007). Although this may suggest similar constraints on the
cranial system in the two groups (maintenance of fiber length; constraints on
cranial mass), it should be noted that the data for the strepsirrhines were
scaled to body mass and may potentially be confounded by allometric changes in
cranial length.
Interestingly, our interspecific analysis shows that across all species,
overall cranial size, muscle masses and the fiber length of the m. temporalis
are the best predictors of bite force as calculated by our model. Species with
a larger cranium, a larger m. temporalis mass and shorter m. temporalis fiber
lengths bite harder. Moreover, these results are identical when taking into
account the phylogenetic relationships between species. Evolutionary changes
in bite force are thus associated with changes in cranial length, m.
temporalis mass and m. temporalis fiber length. Given that fiber length may
constrain the gape angles at which force can be optimally produced
(Taylor and Vinyard, 2004),
this suggests that animals that have evolved high bite forces may be
restricted to eat relatively smaller prey. Our data do, however, suggest a
discrepancy between analyses run with calculated versus in vivo bite
forces, with m. temporalis fiber length no longer contributing to variation in
bite force in the latter analysis. Although this may be a sample size issue,
more data are needed for a wider range of species to test the relevance of
this finding.
In summary, our data suggest an important role for cranial size and the m. temporalis muscle in the evolution towards increased bite performance in bats. Although our analyses suggest no constraint on mass as predicted a priori, scaling analyses suggest that mass constraints may operate for larger-bodied animals. Moreover, our data hint at a potential trade-off between increased bite performance and food size as m. temporalis fiber length is an important determinant of bite force in bats. Finally, the results of this study demonstrate the usefulness and applicability of simple mechanical models in testing morphology–performance relationships across species and suggest that such models may also be used to test hypotheses of cranial design variation associated with differences in feeding strategy in bats.
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Acknowledgments |
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