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The mammalian masticatory rhythm is produced by a brainstem timing network. The rhythm is relatively fixed within individual animals but scales allometrically with body mass (M(b)) across species. It has been hypothesized that sensory feedback and feed-forward adjust the rhythm to match the jaw's natural resonance frequency, with allometric scaling being an observable consequence. However, studies performed with adult animals show that the rhythm is not affected by jaw mass manipulations, indicating that either developmental or evolutionary mechanisms are required for allometry to become manifest. The present study was performed to tease out the relative effects of development versus natural selection on chewing rate allometry. Thirty-one dog breeds and 31 mass-matched non-domestic mammalian species with a range in M(b) from approximately 2 kg to 50 kg were studied. Results demonstrated that the chewing rhythm did not scale with M(b) among dog breeds (R=0.299, P>0.10) or with jaw length (L(j)) (R=0.328, P>0.05). However, there was a significant relationship between the chewing rhythm and M(b) among the non-domestic mammals (R=0.634, P<0.001). These results indicate that scaling is not necessary in the adult animal. We conclude that the central timing network and related sensorimotor systems may be necessary for rhythm generation but they do not explain the 1/3rd to 1/4th allometric scaling observed among adult mammals. The rhythm of the timing network is either adjusted to the physical parameters of the jaw system during early development only, is genetically determined independently of the jaw system or is uniquely hard-wired among dogs and laboratory rodents.
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Mastication, like locomotion and respiration, is a rhythmic behavior
found among many if not most mammalian species. According to
neurophysiological investigators, the rhythm is produced by a
population of brainstem cells known as a central timing network
(CTN) or central rhythm generator (CRG); these cells form a
subcomponent of a larger masticatory central pattern generator
(CPG) network (Lund, 1991; Nakamura and Katakura, 1995). The
traditional model of the masticatory CPG circuitry placed CTN cells
within a fairly localized brainstem region (Nakamura and Katakura,
1995) but further investigations suggest that the rhythmicity is more
diffusely represented (Enomoto et al., 2006; Lund et al., 1998;
Nakamura et al., 1999; Tanaka et al., 1999; Tsuboi et al., 2003; Wu
et al., 2001). Also, although the masticatory CPG is believed to be
highly conserved (Lund et al., 1998; Wainwright, 2002), it is likely
that many underappreciated taxonomic specificities in CTN design
and location exist (Alfaro and Herrel, 2001).
The masticatory rhythm produced by the CTN is surprisingly
fixed within individual animals (Carvalho and Gerstner, 2004) and
within species (e.g. Dellow and Lund, 1971; Horio and Kawamura,
1989; Morimoto et al., 1985; Wainwright, 2002). However, the
masticatory rhythm scales allometrically with body mass (Mb) across
mammalian species groups (Druzinsky, 1993; Gerstner and Gerstein,
2008). It is unclear how and why the scaling relationship exists but
its existence suggests that the relatively fixed rhythmic output of
the CTN is somehow linked broadly across the mammalian class
to morphological size or mass parameters.
Bonner and Horn review several possible explanations for the
presence of allometric scaling in general (Bonner and Horn, 2000).
On the one hand, biologists such as D’Arcy Thompson (Thompson,
1942) have argued that ‘mathematical–physical explanations [are]
sufficient to enforce optima automatically with size changes’ (Bonner
and Horn, 2000). For the school of structuralism, natural selection
and genetics are often irrelevant in scaling (Bonner and Horn, 2000).
With respect to the observed relationship between mastication and
Mb, the mathematical–physical explanation would probably involve
an underlying geometric or physical principle that requires further
explication as applied to rhythmic jaw movement production.
On the other hand, Bonner and Horn prefer the argument that
natural selection is responsible for manifestations of allometric
scaling (Bonner and Horn, 2000). In their argument, allometric
scaling emerges because fitness is greater for individuals that
manifest the appropriate or optimal scaling than for animals
manifesting variation that does not scale.
One way that allometric scaling of the masticatory rhythm could
occur involves sensory systems. Sensory feedback, feed-forward or
both could exploit information about mass-relevant parameters to
adapt the masticatory rhythm accordingly. Such sensory information
could be used in relatively fast time scales, i.e. chew-by-chew or
bite-by-bite, or in developmental time (Pearson, 2000) to affect the
observed scaling. In either case, allometry would result from
physiological adaptations to sensory activity levels in physiological
time scales. The implication is that an archetypal mammalian CPG
network, appropriately coupled to sensory systems, would be
necessary and sufficient to result in the observed allometric scaling
of chewing rate across species.
However, evidence indicates that mass-related adaptations in
chewing rate do not occur in such fast time scales. In acute
The Journal of Experimental Biology 213, 2266-2272
© 2010. Published by The Company of Biologists Ltd
Chewing rates among domestic dog breeds
Geoffrey E. Gerstner1,*, Meghan Cooper2and Peter Helvie3
1Department of Biologic and Materials Sciences, School of Dentistry, Department of Psychology, University of Michigan, Ann Arbor,
MI 48109-1078, USA, 2School of Dental Medicine, Harvard University, Boston, MA 02115, USA and 3Department of Biology,
University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3280, USA
*Author for correspondence (
Accepted 15 March 2010
The mammalian masticatory rhythm is produced by a brainstem timing network. The rhythm is relatively fixed within individual
animals but scales allometrically with body mass (
b) across species. It has been hypothesized that sensory feedback and feed-
forward adjust the rhythm to match the jaw’s natural resonance frequency, with allometric scaling being an observable
consequence. However, studies performed with adult animals show that the rhythm is not affected by jaw mass manipulations,
indicating that either developmental or evolutionary mechanisms are required for allometry to become manifest. The present
study was performed to tease out the relative effects of development
natural selection on chewing rate allometry. Thirty-
one dog breeds and 31 mass-matched non-domestic mammalian species with a range in
bfrom ~2kg to 50kg were studied.
Results demonstrated that the chewing rhythm did not scale with
bamong dog breeds (
>0.10) or with jaw length (
>0.05). However, there was a significant relationship between the chewing rhythm and
bamong the non-domestic
mammals (
<0.001). These results indicate that scaling is not necessary in the adult animal. We conclude that the central
timing network and related sensorimotor systems may be necessary for rhythm generation but they do not explain the 1/3rd to
1/4th allometric scaling observed among adult mammals. The rhythm of the timing network is either adjusted to the physical
parameters of the jaw system during early development only, is genetically determined independently of the jaw system or is
uniquely hard-wired among dogs and laboratory rodents.
Key words: mastication, central pattern generator, motor behavior rhythm.
2267Chewing rates in domestic dogs
experiments on guinea pigs, experimentally evoked rhythmic jaw
movements maintained a constant rhythm when up to 15g of weight
were added to the mandible, in which the manipulation increased
the jaw mass (Mj) by nearly an order of magnitude (Chandler et al.,
1985). These results indicate that sensory feedback is used to
modulate muscle recruitment in response to varying loads on the
jaw. The result is that chewing rate remains relatively constant within
individual animals despite variation in load or Mj. A similar finding
was made for studies of primates, wherein it was discovered that
rate modulation rather than time modulation occurred probably to
render chewing rate relatively invariant (Ross et al., 2007).
More recently, we demonstrated that nearly doubling Mjin adult
rats did not lead to load-dependent changes in licking rate over 12-
week time periods (Carvalho and Gerstner, 2004). We also showed
that individual animals could be reliably identified by mean licking
rates over this time period as well. This suggests that sensory
information does not provide feedback capable of modifying oral
motor rhythm rates in adult animals over relatively long (multi-
month) time scales.
However, the above experimental studies of rats and guinea pigs
evaluated adult animals. Whether the chewing rate–mass relationship
emerges in evolutionary time scales or is a result of ontogenetic
processes operating on proximate mechanisms involving CPG
coupling with sensory information cannot be well demonstrated in
these studies.
In the present study, we attempted to tease apart this issue by
evaluating chewing rate scaling among dog breeds versus among
matched, non-domestic, mammalian species. Dog breeds are
derived from a common wild ancestor, and they show considerable
variation in Mbfor a single species (Parker et al., 2004; Vila et
al., 1997). In our study, dog breeders can be thought of as having
participated in an experiment to vary the independent variables
of Mband jaw length (Lj) while remaining blind to, i.e. relatively
disinterested in artificially selecting for the dependent variable
of interest, chewing rate. Consequently, dog breeds of varying
sizes provide an important resource to examine whether the
mammalian CTN and sensory pathways provide both necessary
and sufficient explanation for allometric scaling between
masticatory rhythm and Mbthat may emerge in developmental
time scales.
Importantly, any natural selection pressure operating specifically
on the relationship between Mband chewing rate, which would be
so important to Bonner’s and Horn’s argument (Bonner and Horn,
2000), has been relaxed among dog breeds but not among the non-
domestic mammalian species we have included in the study. This
allows for a test of the contributory role of natural selection in
allometric scaling. If canine chewing rate is capable of responding
and adapting to mass-related parameters in developmental time
scales as a result of properties inherent in an ‘archetypal’ mammalian
masticatory CTN, or as a result of mathematical–physical
imperatives (Thompson, 1942), then chewing rate and Mbor Mj
should scale among dog breeds as it does among other mammals.
By contrast, if chewing rate does not scale among dog breeds, this
would argue that the mammalian masticatory neuromotor system
is not sufficient to explain the allometric scaling observed among
other mammalian species.
Also, by matching the Mbof dog breeds with the Mbof non-
domestic mammalian species, we attempt to control for statistical
and biological noise sources as well. In other words, by using the
non-domestic mammalian species as a control group, we can
exclude the contributions from noise sources to the negative results
observed in the dog data.
Healthy pure-bred adult dogs from American Kennel Club (AKC)
registered breeders, rescue organizations or dog shows held in
Michigan were chosen as subjects. Between four and seven
individuals were studied per breed, and a total of 31 breeds were
used in the study (Table1). All individuals were pure-bred according
to AKC standards, and ranged in age from 4 to 168 months. Exact
ages were not recorded for the dogs at one AKC dog show; however,
the handlers all confirmed that their dogs were adult dogs over 12
months in age.
This investigation reports negative results for some analyses.
Therefore, it was important to demonstrate that these negative results
were not due to statistical noise, i.e. variability in the scatter plot,
overwhelming the relatively narrow range of Mbrepresented by the
dog breeds, e.g. cf. p. vii and p. 38 in Calder (Calder, 1996). To
this end, each dog breed was matched to a non-domestic mammalian
species with a similar Mb. This was done by first calculating the
mean Mbfor each dog breed in this study. Next, a non-domestic
mammalian species was selected from previously tabulated sources
(Gerstner and Gerstein, 2008), such that each dog breed was closely
matched to a given mammalian species according to Mb. This was
done while remaining blind to the chewing rates of the dog breeds
and non-domestic mammalian species. Once the matching was done,
the previously reported chewing rates for the matched, non-domestic,
mammalian species (Gerstner and Gerstein, 2008) were used in the
Chewing task
Individual dogs were fed by their owners or the investigator while
the investigator videotaped the chewing sequence. Dogs were fed
treats that owners typically fed their dogs, which included dry
chicken jerky, small pieces of crushed rawhide or dry dog food
treats. The standardization of bite size was considered; however,
in cats, larger bite sizes are associated with longer cycle durations
(Thexton et al., 1980). If a similar relationship were to exist in
the case of dogs, then standardized bite sizes would result in
relatively fast chewing rates in large breeds and relatively slow
chewing rates in small breeds, a relationship opposite to that
observed to exist between mammalian Mband chewing rate
(Druzinsky, 1993; Gerstner and Gerstein, 2008). Thus, it was
decided to allow dogs to receive bite sizes to which they were
acclimated and which were commercially designed for breed sizes.
This meant that larger dog breeds typically received larger bite
sizes than smaller dog breeds.
Previous results have indicated that chewing rate does not vary
considerably with changing food hardness (Thexton et al., 1980;
Yamada and Yamamura, 1996). Therefore, the hardness of the treat
was dependent on the typical treat given to each dog and left up to
the discretion of the owner.
Chewing sequences were videotaped (JVC Digital Video Camera,
model GRDVL720U, Aurora, IL, USA) with auto-iris ‘on’ and
digital shutter speed60Hz. Animals were videotaped in the sagittal
or frontal view, and the camera zoomed to focus mainly on the head
and neck. A priori, it was determined that single chews would be
omitted from analysis, and that only bursts of at least three
consecutive and rhythmical chews would be used in analyses.
Therefore, individual animals were videotaped so that bursts of at
least three consecutive chews were obtained. The investigator
continued to videotape the dog chewing until ~30 total analyzable
chews, i.e. 30 chews occurring in bursts consisting of >3 chews,
were recorded.
Data collection
Canine chewing sequences were recorded to digital videotape (Sony
premium Mini DV, Tokyo, Japan, or Panasonic Mini DV, Secaucus,
NJ, USA). The videotaped clips were digitized at a rate of
29.97framess–1 using Final Cut Pro 6.06 (Apple Inc., Cupertino,
CA, USA) on a Mac OS X system (Apple Inc.) and a Sony Digital
HD Videocassette Recorder to convert the data from Mini DV to a
digital format that could be used in Final Cut Pro. Based on previous
work with cats (Thexton et al., 1980; Yamada and Yamamura, 1996)
and on preliminary canine data obtained in this study, this capture
rate was considered adequate for chewing cycle duration (CD)
Data analysis
CD were determined by watching the digitized video sequences,
frame-by-frame and recording the total number of frames, Nf, and
the total number of chewing cycles, Nc, in the sequence. A chewing
cycle was defined by the period of time between consecutive
maximum jaw openings. The mean CD for a sequence was
calculated by dividing the ratio, Nf/Nc, by the frame rate,
29.97framess–1. When multiple chewing bursts were analyzed for
a given animal, a weighted mean CD was calculated for that animal.
Each breed was represented by at least four animals, and the breed
mean CD was calculated by using the mean CD for each animal
sampled in the given breed.
Many allometric studies use Mbas the independent variable, and
in this study, certain analyses used Mbas well. However, many dog-
G. E. Gerstner, M. Cooper and P. Helvie
breed heads have unique morphologies that may vary independently
of Mb. Furthermore, it has been demonstrated that jaw lever arm
lengths correlate with CD among primates (Ross et al., 2009).
Therefore, a mean Ljwas also obtained for each breed. Ljwas
measured by the investigator directly on the dogs at the time that
chewing data were videotaped. The jaw was measured in mm from
the angle of the ramus or gonion to the anterior-most point on the
mandible or pogonion (Fig.1).
Statistical methods
Mean CD, Ljand Mbfor each breed were log-transformed. Linear
regression analyses were used to study the relationships between
log CD, log Mband log Lj. Because a major focus of this
investigation involved intraspecific studies, we assumed a normal
bivariate distribution and used ordinary least-squares regression as
suggested by previous allometric investigations (Wehner et al., 2007)
[see also p. vii in Calder (Calder, 1996)].
Table1 contains the dog breeds, number of dogs (N) and means for
Mb, CD and Lj. One s.d. is shown in parentheses for each of the
means. Also shown in Table1 is a list of the non-domestic
mammalian species, arranged according to the dog breeds to which
they were matched during the selection phase of the study (see
‘Animals’ in Materials and methods). Mammalian Mb and CD are
shown as reported in previous work (Gerstner and Gerstein, 2008).
Note in Table1 that three dog breeds had relatively large s.d.
Table 1. Animal subject statistics*
‘Paired’ mammal
Dog breed
b(g) CD (ms)
j(cm) Genus species
b(g) CD (ms)
Afghan Hound 4 22,226 (3648) 531 (284) 20.5 (1.6)
Capreolus capreolus
20,000 612
Alaskan Malamute 4 35,834 (9384) 426 (42) 19.4 (1.8)
Macropus rufus
35,000 538
Beagle 4 9412 (2495) 334 (27) 15.2 (1.1)
Ovis dalli
10,500 493
Belgian Tervuren 4 23,869 (2572) 389 (42) 19.2 (0.7)
Chrysocyon brachyurus
23,000 390
Bernese Mountain Dog 4 40,938 (5824) 339 (2) 19.9 (1.6)
Panthera pardus
43,000 580
Bouvier des Flandres 4 39,349 (8617) 442 (58) 21.2 (0.9)
Capra ibex
40,000 597
Brittany 4 18,711 (4277) 359 (31) 17.0 (1.4)
Pecari tajacu
19,000 480
Chihuahua 4 2313 (732) 350 (72) 5.6 (0.6)
Pedetes capensis
2300 317
Chow Chow 4 27,216 (4670) 392 (35) 18.3 (0.6)
Macropus robustus
24,900 455
Collie 4 24,948 (5238) 461 (85) 21.8 (1.3)
Eudorcas thomsonii
24,000 450
Coonhound 4 31,184 (2830) 394 (23) 19.9 (0.1)
Canis lupus
32,000 280
Dachshund 5 6078 (1654) 362 (32) 11.2 (0.9)
Hylobates lar
6000 374
English Setter 4 27,783 (2602) 367 (33) 18.5 (0.6)
Macropus giganteus
30,000 499
Flat-coated Retriever 4 32,659 (2963) 357 (31) 19.2 (2.7)
Pongo pygmaeus
37,000 660
German Shorthaired Pointer 4 24,381 (6514) 375 (120) 20.0 (1.4)
Sus scrofa
22,300 330
Golden Retriever 4 31,298 (8413) 484 (95) 20.0 (1.2)
Antilope cervicapra
36,000 493
Greyhound 4 26,989 (4327) 388 (39) 20.1 (0.2)
Gazella subgutturosa
20,000 880
Italian Greyhound 4 4763 (1361) 383 (52) 10.7 (3.2)
Nasua nasua
4800 258
Keeshond 4 15,649 (1870) 654 (577) 16.0 (0.6)
Lontra canadensis
15,000 280
Kerry Blue Terrier 4 17,350 (2264) 386 (47) 17.7 (0.3)
Muntiacus muntjak
18,000 565
Newfoundland 4 54,204 (10889) 384 (117) 19.9 (2.0)
Axis axis
53,000 496
Norfolk Terrier 4 6691 (1630) 321 (31) 11.3 (0.2)
6000 326
Pekingese 4 4858 (713) 354 (43) 6.9 (0.6)
Philantomba monticola
4500 358
Pomeranian 4 2041 (586) 325 (37) 7.8 (0.3)
Lemur catta
2500 270
Rhodesian Ridgeback 4 32,885 (4707) 462 (27) 19.9 (0.4)
Capra hircus
40,000 600
Rottweiller 4 40,030 (8593) 404 (58) 19.0 (1.9)
Pan troglodytes
40,000 540
Rough Collie 4 30,843 (6297) 398 (69) 22.2 (0.7)
Antidorcas marsupialis
37,500 562
Samoyed 4 19,845 (1499) 400 (22) 20.2 (2.2)
Macaca fuscata
15,000 420
Siberian Husky 4 20,865 (4107) 814 (427) 18.6 (1.3)
Mandrillus sphinx
18,000 556
West Highland Terrier 4 8165 (1614) 439 (48) 12.8 (0.6)
Erythrocebus patas
8500 405
Whippet 7 9720 (3280) 348 (77) 15.5 (1.2)
Muntiacus reevesi
11,000 413
*Canine body mass (
b), chew duration (CD) and jaw length (
j) means (s.d.).
Mammalian data sources same as in Gerstner and Gerstein (Gerstner and Gerstein, 2008); mammalian taxonomic authority was Wilson and Reeder (Wilson
and Reeder, 2005).
2269Chewing rates in domestic dogs
associated with CD, viz. Afghan (284), Keeshond (577) and Siberian
Husky (427). In all three instances, this was due to one individual
dog, each of which was characterized by a relatively long-duration
mean CD. Respectively, the breed means dropped to 390ms,
366ms and 603ms for Afghan, Keeshond and Husky when these
three dogs were removed. Certain analyses, below, were performed
first with and then without these three individual dogs.
Fig.2 compares CD with Mbfor the 31 dog breeds. Log CD was
not significantly related to log Mb(R0.2992, d.f.29, P>0.10;
scaling exponent0.0691). However, after removing the one Afghan,
one Keeshond and one Siberian Husky dog responsible for inflating
the s.d. for these breeds (Table1), there was a significant correlation
for the comparison between canine log CD and log Mb(R0.440,
d.f.29, P<0.014). Nevertheless, it should be stressed that this
significant correlation only accounted for about 19% of the variation
between CD and Mb. In this latter case, the equation of the line was
y0.0668x+2.308. The 95% confidence interval (CI) on the
In allometric studies, it is recognized that relatively narrow ranges
of Mbcan have profound impacts on correlations between Mband
a dependent variable of interest [see Introduction to this paper and
p. vii and 38 in Calder (Calder, 1996)]. Given that the dog-breeds’
range of Mb(Table1) was relatively narrow compared with that
found in many allometric studies, it was important to demonstrate
whether this narrow range played a role in the observed lack of
significance between Mband CD among dogs. This was a major
reason for analyzing data from the non-domestic mammals
representing a similar mass range as the dogs (Table1). Fig.3
provides a visualization of the matching achieved between canine
and mammalian Mb. Plotted are the paired log Mbfor the dogs (x-
axis) and mammals (y-axis), along with a least-squares regression
line to demonstrate the relative closeness of the matching. In the
plot, y0.9892x–0.0454, R20.98. Note that the slope is near unity,
the intercept is near the origin and that R2is high, all of which
provide an indication of how closely matched the two groups were
in terms of Mb. The means (s.d.) Mbin kg for dogs22.4 (13.2) and
for mammals22.5 (13.2) were not significantly different (paired
t0.72, P>0.05).
An analysis similar to that depicted in Fig.2 was performed on
the non-domestic mammals shown in Table1. For these mammals,
a highly significant relationship between log Mband log CD was
found (Fig.4, R0.634, d.f.29, P<0.0001). Here, the slope or scaling
exponent0.220 and the y-intercept1.721. The 95% CI on the
slope0.118–0.321, slightly overlapping the 95% CI for the slope
determined for the canine data with the outliers removed; however,
the slope for the dogs (0.0668) was over 3 times less than the 0.220
slope for the mammals.
Recent studies have begun modeling relationships between CD
and jaw lever arm lengths among mammals (Ross et al., 2009). Fig.5
Fig.1. Depiction of jaw length (
j) (see text).
3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00
Fig.2. Relationship between body mass (
b) and chew duration (CD) in
dog breeds.
3.0 3.5 4.0 4.5 5.0
Fig.3. Relationship between body mass (
b) in dog breeds and
bin non-
domestic mammalian species, plotted as matched pairs (see Table1 and
3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00
Fig.4. Relationship between body mass (
b) and chew duration (CD)
among representative non-domestic mammals.
plots log Ljagainst log CD for the 31 dog breeds, along with a least-
squares regression line. The relationship was not significant
(R0.3283, d.f.29, P0.0756; scaling exponent0.1997,
intercept2.2289). However, when the three outlier dogs, viz. one
Afghan, one Keeshond and one Siberian Husky, were removed from
the analysis, the relationship was significant (R0.4411, d.f.29,
P0.013). In this latter case, the analysis yielded the following
allometric equation, y0.1628x+2.2311. The 95% CI for the scaling
exponent0.0370–0.289. However, only 19% of the variation in CD
was explained by variation in Lj.
The 1/4th (scaling exponent ~0.25) to 1/3rd (scaling exponent ~0.33
or ~0.167) allometric relationship between Mband CD found in
most mammals (Druzinsky, 1993; Gerstner and Gerstein, 2008; Ross
et al., 2009) was not found among adult dog breeds in the present
study (Fig.2). Without the included mammalian data in the study
(Table1 and Fig.4), it may have been possible to conclude that the
lack of scaling was the result of the increased role of error in a data
set where the range of Mbwas relatively small. Previous allometric
studies of mammalian chewing have involved Mbdata representing
about five orders of magnitude (Druzinsky, 1993; Gerstner and
Gerstein, 2008) whereas the present study involved less than two
orders of magnitude in Mb. Although this reduction in the range of
Mbwas a contributing factor to the reduced correlation observed in
mammals [compare Fig.4 results with larger data sets in Druzinsky,
and Gerstner and Gerstein (Druzinsky, 1993; Gerstner and Gerstein,
2008)], it was clearly not sufficient to render the correlation
insignificant among mammals. In fact, the correlation between Mb
and CD among the mammals size-matched with the dog breeds was
highly significant. In this sense, the mammalian results in the present
study render the interpretation of the canine negative results more
It is also important to emphasize that the role of error was probably
greater in the mammalian data (Fig.4) than in the dog breed data
(Fig.2) for several reasons. First, CD and Mbwere often not sampled
from the same individuals in many of the mammals (Gerstner and
Gerstein, 2008) whereas both variables were sampled from the same
individual dogs. Second, the foodstuffs eaten were more variable
among the mammals than among the dog breeds; hence, the extent
to which food parameters play a role in modifying chewing rate
(Thexton et al., 1980) would have been greater among the mammals
than among the dogs. Finally, each canine datum represented the mean
G. E. Gerstner, M. Cooper and P. Helvie
of at least four animals whereas most of the mammalian data were
represented by fewer than four individual animals and in many cases
by one animal only. For these reasons, the scatter in the mammalian
data in Fig.4 is likely to be inflated relative to the scatter in the dog-
breed data in Fig.2. The fact that a highly significant correlation still
remained for the mammalian data underscores the robustness of
allometric scaling even under suboptimal sampling conditions.
Furthermore, the fact that the scaling was absent from the dog data,
except when the ‘outlier’ data were removed is all the more
demonstrative that CD–Mbscaling in adult mammals may not be
guaranteed under certain artificial selection conditions.
Table1 shows that the wolf, Canis lupus, had an Mb32,000g,
which is greater than 24 of the dog breeds. However, the wolf
CD280ms was of shorter duration than all dog breeds studied. By
contrast, a comparison of Figs2 and 4, the abscissas and ordinates
of which have the same ranges, will reveal considerable overlap in
the scatter of data points between dogs and mammals. These results
demonstrate that the dog breeds’ CDs were slower than the wolf
CD but that the dog CD range was similar to the mammalian CD
range. This suggests that whatever selection pressure may be
responsible for the relatively rapid wolf CD is missing, altered or
reduced among the domestic dog breeds.
Several possibilities for this finding exist. For one, the wolf data
in our study could represent relatively fast-chewing wolves. Perhaps
the wolves from which all modern dog breeds are derived were
slower chewers, more similar to the mean chewing rate of modern
dog breeds.
Alternatively, dog-breed CDs may be ‘drifting’ towards more Mb-
typical or mammalian-class-typical CDs. However, it is unclear what
artificial selection pressure would have led towards a biased slowing
of CD in all dog breeds, both large and small. If random drift in
CD has occurred, dog breed CD would be expected to be both greater
than and less than the wolf CD. Some other biological variable is
likely to be contributing to the across-breed slowing in CD. We
hypothesize that it has something to do with development, and we
will discuss this hypothesis further, below.
It is important to comment on the statistically significant
relationship between CD and Mband between CD and Ljthat
occurred when outlier data were removed, and to explore the reason
for the relatively low slope of the regression line under these
circumstances. When the three outlier dogs were removed from the
analysis, the scaling exponent (slope) for the comparison of Mband
CD was 0.0668 (95% CI0.0149–0.119) for dogs (Fig.2) whereas
the same comparison yielded a slope of 0.220 (95%
CI0.118–0.321) for mammals. These data are summarized in
Table2. Although the 95% CI for dogs versus mammals slightly
overlapped, the slope for the dogs was ~3 times lower than that for
the mammals. Importantly, the 95% CI for the dog data did not
include the 1/3rd or 1/4th scaling exponent previously reported in
mammals (Druzinsky, 1993; Gerstner and Gerstein, 2008); the slope
and 95% CI for previously reported mammalian work is presented
in Table2 for comparison, along with results for carnivores
specifically (cf. Gerstner and Gerstein, 2008). This suggests that a
different scaling rule is involved in the relationship between CD
and Mbin the dog data, if one is to accept as valid the removal of
the ‘outlier’ individual dogs in order to achieve statistical
significance. Moreover, whatever this scaling rule might be, the
relationship between Mband CD for dogs sans outliers accounted
for only 19% of the variation compared with 40% of the variation
being accounted for in the mammalian relationship (Fig.4). Clearly,
whatever rule governs the Mb–CD relationship, it is much weaker
among dogs than among mammals.
1.5 1.6 1.7 1.81.9 2 2.1
Fig.5. Relationship between jaw length (
j) and chew duration (CD) in dog
2271Chewing rates in domestic dogs
By the same token, the scaling exponent for comparison of Lj
and CD was 0.163 for dogs sans the three outliers (95%
CI0.0370–0.289), compared with scaling exponents ranging from
0.514 to 0.583 for non-primate mammals and primates, respectively
(Ross et al., 2009). The last three rows in Table2 replicate the Ross
et al. data along with the present study’s dog data for comparison.
Note that the 95% CI for CD and Ljfor the dogs versus the Ross
et al. mammals does not overlap, and that the slope for the dogs is,
again, ~3 times shallower than for these other mammals (Table2).
One explanation for these differences may be due to the different
developmental trajectories of dogs versus non-domestic mammals.
Infant size tends to scale with adult body size in non-domestic
mammalian species (cf. Calder, 1996), which means that CD would
scale proportionally with both infant and adult Mbin non-domestic
animals. By contrast, pre-weaned puppy sizes are relatively similar
across dog breeds of vastly different adult sizes (Hawthorne et al.,
2004). In fact, using the logistic equation for growth in dog breeds
presented by Hawthorne et al., i.e. Mba/{1+exp[–(xx0)/b]}, their
data for 12 dog breeds [table1 in Hawthorne et al. (Hawthorne et
al., 2004)], and assuming a weaning age of 4 weeks for dog breeds,
we calculated a 13.4-fold range of Mbfor 4-week old dogs compared
with a 30.4-fold range of Mbfor adult dogs. In other words, breed
size-differences are about 2.3 times greater across adult dog breeds
than across puppies of the corresponding breeds. If Mj-related CD
adjustments can occur during infant stages only, then one would
expect the canine CD–Mbscaling exponent to be 2.3 times less than
predicted based on adult dog sizes. In our data, the scaling exponent
was 3 times less than expected. This suggests that part of the
explanation for the reduced scaling exponent in our canine results
compared with other mammalian results (Table2) may be due to
developmental effects.
The brainstem is likely to be more plastic in immature animals
when circuits are first being constructed and formed. Previous
studies have demonstrated that increasing the mass of the adult
jaw does not lead to corresponding changes in CD in adult
mammals (Carvalho and Gerstner, 2004; Chandler et al., 1985).
This strongly suggests that CD cannot be adjusted to mass-related
changes in adult animals. Consequently, if mass-related
adjustments in CD can occur in immature animals and brainstems,
then the observed CD in adult animals would mark the
developmental stage at which mass-associated adjustments in CD
are no longer possible. Subsequent jaw growth would then occur
in the absence of corresponding CD modifications, after which
developmental time point, sensory feedback would result in
increased muscle recruitment to adjust to further increases in jaw
size. In mammals, CD would appear to be adjusted to mass in
the adult animals because of the infant–adult isometric scaling
(Calder, 1996). By contrast, jaw growth among dog breeds varies
considerably, and this would provide an important explanation
for the scaling-exponent findings of the present study. Further
developmental studies are in order to test this possibility.
It is also compelling that the relative similarity in puppy sizes
across dog breeds apparently represents an adaptation for suckling
(Coppinger and Coppinger, 2002). Adapting mammalian CTN
output to match infant Ljwould make particular sense if, for instance,
suckling efficiency is more important than chewing efficiency in
terms of fitness or fecundity. Future studies will need to test these
hypotheses through comparative developmental investigations to sort
out whether there is a developmental time period during which mass-
related oral rhythm adjustments occur, whether this time period
represents a definitive point in neural ontogenesis across mammals,
whether there are significant fitness or fecundity costs associated
with mismatches at this developmental stage, etc.
It is also noteworthy that the above discussion allows for the
possibility of an archetypal mammalian CTN whose rhythmic output
is adjusted to species-typical and individual-specific parameters in
developmental time scales. Then, at some point, the rhythmicity
becomes fixed, after which point changes in load are associated with
adaptations in muscle recruitment (Ross et al., 2007). In this case
one important question becomes, at what point in development do
load-dependent adjustments in CTN output shift from modifications
in rhythmic rates to modifications in muscle recruitment.
It has been argued that a movement’s rhythmicity is a product
of sensory feedback and feed-forward (Kuo, 2002). The ‘strong’
interpretation of this argument is that a specific rhythm is not actually
embodied in the nervous system but is rather an emergent property
of feedback and feed-forward between a central oscillatory model
and the physical properties of the moving body part. This may be
true for human locomotion (Kuo, 2002); however, evidence from
adult animal studies strongly suggests that masticatory rhythmicity
is produced centrally (Carvalho and Gerstner, 2004; Chandler et al.,
1985). Based on the present study’s results, we conclude that
mammalian oral rhythms probably become centrally embodied
during development, in which case there may be an early
developmental period during which Kuo’s argument is tenable (Kuo,
2002). However, if masticatory rhythmicity proves to be
unresponsive to mass-related variation at any point during
development, but emerges early and independent of feedback, this
would suggest that the CTN is genetically inherited.
The central embodiment and heritability of the masticatory
rhythm is supported by much previous work. Food properties do
not affect chewing rate in rabbits (Morimoto et al., 1985; Yamada
and Yamamura, 1996), humans (Horio and Kawamura, 1989),
monkeys or cats (reviewed in Inoue et al., 1989). Licking in rats
appears to be controlled by ‘a central timing mechanism that is
somewhat impervious to disturbance’ (Travers et al., 1997). Indeed,
the oral rhythm generator in rats has been called ‘particularly rigid’
and is little affected by water deprivation, taste, environmental
Table 2. Statistics and regression results involving body mass (
b) or jaw length (
j) regressed against chew duration (CD) in specific
mammalian groups
Variable Slope 95% CI Source (method)
Dog breeds 31
b0.0668 0.0149 0.119 Present study (LSR)
Mammals 31
b0.220 0.118 0.321 Present study (LSR)
Mammals 132
b0.152 0.133 0.171 Gerstner and Gerstein, 2008 (LSR)
Carnivora 20
b0.147 0.0832 0.210 Gerstner and Gerstein, 2008 (LSR)
Dog breeds 31
j0.163 0.037 0.289 Present study (LSR)
Non-primates 43
j0.514 0.4435 0.5949 Ross et al., 2009 (RMA)
Primates 36
j0.583 0.4641 0.7318 Ross et al., 2009 (RMA)
Key: least-squares regression (LSR), confidence interval (CI), reduced major axis (RMA).
2272 G. E. Gerstner, M. Cooper and P. Helvie
modifications, behavior conditioning or behavior modification
experiments (Bures et al., 1988). Oral rhythmicity is not prolonged
by increasing Mjin either guinea pigs (Chandler et al., 1985) or rats
(Carvalho and Gerstner, 2004). These studies, which have involved
numerous mammalian species, provide compelling evidence that
oral rhythms in adult animals are centrally generated, nearly
impervious to modification and apparently designed to be rigid (e.g.
Ross et al., 2007). In light of these studies, the argument has been
made by other investigators than ourselves that the rhythmicity is
heritable (Kobayashi et al., 2002).
Moreover, given that chewing rate does not necessarily scale to
adult Mb, we would argue that either there is an ‘archetypal’
mammalian CTN that adjusts its rhythmicity to the mass of the
developing animal, but only up to a specific point in time (as
discussed above), or that each mammalian species must possess
unique species-specific CTN components that result in CD being
scaled to Mb. The unique solutions would be due largely to the unique
histories of each phylogenetic lineage or clade. This argument
corroborates arguments made elsewhere about vertebrate feeding
in general (Alfaro and Herrel, 2001). This has profound implication
for health sciences, which rely on a relative few species models to
provide insight into the human condition. Future research will be
required to address the nature of chewing rate heritability, of
homology and analogy in mammalian CTN (Alfaro and Herrel,
2001), and how genetic, epigenetic and environmental parameters
during development interact to produce not only species-typical
chewing rates but the individual specificity that has also been
reported (Carvalho and Gerstner, 2004).
AKC American Kennel Club
CD chew duration
CI confidence interval
CPG central pattern generator
CRG central rhythm generator
CTN central timing network
Ljjaw length
Mbbody mass
Mjjaw mass
Ncnumber of chewing cycles
Nfnumber of frames
s.d. standard deviation
We wish to thank Brian Sackett and Jonathan Gerstein for help with data
acquisition and analysis. G.E.G. wishes to dedicate this paper to Duncan, one of
the English setters in the study who initially inspired the study and shuffled off this
mortal coil during the preparation of the manuscript. This work was supported in
part by NIH grant DE10625 to G.E.G. Deposited in PMC for release after 12
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... Scientific RepoRts | 7:43967 | DOI: 10.1038/srep43967 f chew ~ M −1/6 based on pendulum-type movement of jaws 13,14 . Also, frequency variations were considered as statistical noise or randomness, which has generated a variety of scaling laws and aroused controversy between different models. ...
... Measurements of chewing frequency are reported on Fig. 2 over six orders of magnitude of animal mass. Black circles denote data that we measured from Virginia Tech farms, boxed rectangles are data that we estimated from online sources (see Materials and Methods) and triangles are measurements reported by 8,9,[13][14][15] . We denote carnivores, herbivores, and omnivores with red, green, and blue colors, respectively. ...
... (b) Relation between jaw length and animal mass. Data are obtained from 13,[43][44][45] . The dashed line is the best fit with a 0.37 power, whereas the solid line is a 1/3 power as our assumption. ...
Full-text available
Previous studies on chewing frequency across animal species have focused on finding a single universal scaling law. Controversy between the different models has been aroused without elucidating the variations in chewing frequency. In the present study we show that vigorous chewing is limited by the maximum force of muscle, so that the upper chewing frequency scales as the −1/3 power of body mass for large animals and as a constant frequency for small animals. On the other hand, gentle chewing to mix food uniformly without excess of saliva describes the lower limit of chewing frequency, scaling approximately as the −1/6 power of body mass. These physical constraints frame the −1/4 power law classically inferred from allometry of animal metabolic rates. All of our experimental data stay within these physical boundaries over six orders of magnitude of body mass regardless of food types.
... According to Tinbergen's four behavior questions (Tinbergen, 1963;Mayr, 1982), the rhythmic scaling could be a result of: (1) adaptive causes; it could provide a species with reproductive or survival advantages (Bonner & Horn, 2000;Ross et al., 2009b), by improving masticatory performance, reducing tooth wear, reducing feeding time, etc., (2) mechanistic causes; it could result from inherent properties of the neuromotor system, e.g., sensory information could be used to adapt the masticatory rhythm to produce output near the resonant frequency of oral structures (Ross et al., 2007a(Ross et al., , 2007bTurvey, Schmidt, & Rosenblum, 1988), (3) phylogenetic causes that reflect an integrated functional relationship, e.g., ''correlated evolution of physiological and morphological traits'', that may exist across taxa independent of feeding niche (Vinyard et al., 2011), and (4) ontogenetic causes; it could occur during a critical time period when the developing neuromotor system is particularly adaptable to physical characteristics of oral structures (Stover & Williams, 2011;Iinuma, Yoshida, & Funakoshi, 1991;Iinuma, Yoshida, & Funakoshi, 1994), or both chewing rhythm and body size could[ 5 _ T D $ D I F F ] be heritable (Gerstner, Cooper, & Helvie, 2010;Kobayashi et al., 2002). These four causal explanations are not mutually exclusive but rather indicate perspectives for continued research (Cheverud, 1982). ...
... Although numerous oral motor studies aligned with one or more of the above causal approaches have been done, heritability studies have not occurred, despite suggestions that the masticatory rhythm could be heritable (Gerstner et al., 2010;Kobayashi et al., 2002). Mechanistic studies have demonstrated that neither food properties nor experimentally-induced changes in jaw mass appear to affect chewing rate substantially, indicating that neuromotor systems do not appear to respond to sensory information nor jaw mass as mechanistic principles would predict (Thexton et al., 1980;Ross et al., 2007b;Carvalho & Gerstner, 2004). ...
... Mechanistic studies have demonstrated that neither food properties nor experimentally-induced changes in jaw mass appear to affect chewing rate substantially, indicating that neuromotor systems do not appear to respond to sensory information nor jaw mass as mechanistic principles would predict (Thexton et al., 1980;Ross et al., 2007b;Carvalho & Gerstner, 2004). Moreover, scaling does not occur in domestic species that manifest a range of adult body sizes, viz., dogs and horses; rather, all members of the domestic species chew at nearly the same rate despite considerable body mass variation (Stover & Williams, 2011;Gerstner et al., 2010). Furthermore, given that all dog breeds and all horse breeds chew at nearly the same rate independent of body size, this suggests the possibility that chewing rate may be heritable. ...
Objective: To study and compare the relationships between mean chewing cycle duration, selected cephalometric variables representing mandibular length, face height, etc., measured in women and in their teenage or young-adult biological daughters. Design: Daughters were recruited from local high schools and the University of Michigan School of Dentistry. Selection criteria included healthy females with full dentition, 1st molar occlusion, no active orthodontics, no medical conditions nor medication use that could interfere with normal masticatory motor function. Mothers had to be biologically related to their daughters. All data were obtained in the School of Dentistry. Measurements obtained from lateral cephalograms included: two "jaw length" measures, condylion-gnathion and gonion-gnathion, and four measures of facial profile including lower anterior face height, and angles sella-nasion-A point (SNA), sella-nasion-B point (SNB) and A point-nasion-B point (ANB). Mean cycle duration was calculated from 60 continuous chewing cycles, where a cycle was defined as the time between two successive maximum jaw openings in the vertical dimension. Other variables included subject height and weight. Linear and logistic regression analyses were used to evaluate the mother-daughter relationships and to study the relationships between cephalometric variables and chewing cycle duration. Results: Height, weight, Co-Gn and Go-Gn were significantly correlated between mother-daughter pairs; however, mean cycle duration was not (r(2)=0.015). Mean cycle duration was positively correlated with ANB and height in mothers, but negatively correlated with Co-Gn in daughters. Conclusions: Chewing rate is not correlated between mothers and daughters in humans.
... Another study used dogs to test whether variation in adult breed sizes would correlate with variation in breed chewing rhythms. 23 The study showed that dog breeds varying in size from Pomeranians to Mastiffs chewed at a similar frequency, and that T C did not correlate with M B among dog breeds. Importantly, the M B of pre-weaned puppies is fairly similar across dog breeds of vastly different adult M B . ...
... 24 It was, therefore, suggested that the mechanisms governing T C among dogs may only respond to increases in jaw mass within a developmental window that occurs prior to ontogenetic divergence in breed M B . 23 Additionally, each breed was matched with a wild mammal in terms of M B and the wild mammals showed a correlation between T C and M B . Hence, despite the narrowed range in M B among dog breeds, the wild mammal 'control' group demonstrated that a relationship between T C and M B could have been detected if one did, indeed, exist. ...
... It should be noted, however, that comparisons of T C and jaw length across primate groups, 10 across mammals, 11 and across different sized horses 25 and dogs 23 indicates that T C $ (jaw length) 0.2-0.58 . It is not clear why the scaling exponent for comparisons across adult animals is considerably less than the exponent for ontogenetic comparisons. ...
... [8][9][10][11][12] It has been found that the masticatory frequency decreases as M b increases. [13][14][15][16][17][18][19] Small mammals have a shorter chewing cycle duration (CCD) than larger mammals. The slope of the regression line was different in these studies, however. ...
... The slope of the regression line was different in these studies, however. [13][14][15][16][17][18][19] This difference might be due to differences in the method of measuring CCD, or differences in the size and hardness of foods given to the test animals. Also, genetic differences between subpopulations of species in different experiments might be responsible. ...
... 13, 17 The CCD has been found to increase with L m . 13,15,17 Measuring of mandibular morphology has not been made sufficiently, however. ...
... In particular, puppies and adolescent dogs show high chewing activity (Seksel, 2008), the teeth mainly used for chewing are the premolar and molar teeth including the carnassial teeth (upper P4 and lower M1) (Logan, 2006). The mean chewing cycle duration (time between consecutive maximum jaw openings) ranges between 321 and 814 ms and there is a moderate positive correlation with increased body mass and jaw length (Gerstner et al., 2010). ...
Full-text available
Chewing is a behavioural element of feeding, but dogs also chew on or dissect non-edible items. This can cause considerable problems to owners if directed at household objects. Nevertheless, the provision of chewing material, associated risks and relationships with chewing behaviour and other owner-dog activities have not been investigated so far. The aim of this online survey was to explore how dog owners manage the chewing behaviour of their dog and whether there are relationships to other owner-dog interactions. Of our self-selected participants (1439 filled in the entire questionnaire), 94% provided their dogs with edible chewing material (e.g., rawhide, dried innards, meat), 83% provided inedible chew toys, 73% provided chew toys filled with food and 51% provided hard chewing material (e.g. wood, antlers). Edible materials were provided four to six times a week by the average dog owner. Regarding risks, 67% of respondents stated that their dog never had a problem caused by the use of chewing material, whereas veterinary treatment due to a problem with chewing material was reported by 3.6%. Chewing daily on soft household objects was observed in 2.5% of dogs (other common objects for daily chewing: resting places 2.2 %, clothes/shoes 1.4%); dogs up to one year of age did this more frequently (p < 0.001). Chewing on objects was not substantially related to reported motivation of the dog to play or the frequency of activities with the dog (all rs < 0.2), but was reported to occur in contexts that may cause negative emotional states such as leaving the dog alone (rs = 0.63, p < 0.001) or changes in routine activities (rs = 0.47, p < 0.001). The average reported frequency of provision of chewing material correlated positively (rs ≥ 0.2) with motivation of the dog to play, chewing on objects, human-dog play and calm activities such as petting. Dog owners think that chewing material is important for puppies and even more for adult dogs (p < 0.001). However, it remains to be investigated how motivated dogs are for chewing on different types of materials and whether chewing, as proposed by dog professionals, reduces stress. This seems particularly important for assessing the trade-off between risks and benefits of different chewing materials and its impact on dog welfare.
... However, in-vivo rates are very difficult to estimate, since the applied strain rates vary with time during the bite (jaw protrusion) 28 , as well as with position in the food. In addition, the strain rates fundamentally scale up with decreasing food item size and/or with increasing bite speed 28 ; the latter varies significantly between breeds and can be influenced by several factors 42 . As a result of all the above, the tested strain rate range 0.0001−5 s −1 , based on which the viscoplastic-damage law was calibrated here, was chosen as large as possible. ...
Full-text available
Oral biofilm accumulation in pets is a growing concern. It is desirable to address this problem via non-invasive teeth cleaning techniques, such as through friction between teeth and food during chewing. Therefore, pet food design tools are needed towards optimising cleaning efficacy. Developing such tools is challenging, as several parameters affecting teeth cleaning should be considered: the food's complex mechanical response, the contacting surfaces topology as well as the wide range of masticatory and anatomical characteristics amongst breeds. We show that Finite Element (FE) models can efficiently account for all these parameters, through the simulation of food deformation and fracture during the first bite. This reduces the need for time consuming and costly in-vivo or in-vitro trials. Our in-silico model is validated through in-vitro tests, demonstrating that the initial oral processing stage can be engineered through computers with high fidelity.
... He also observed no remarkable correlation between chewing rate and jaw length, which is consistent with previous findings in dog [36] and horse [37] models. Therefore, food consistency can affect children's acquisition of chewing skills and might play a potential role in the development of craniofacial morphology, but future studies are still needed. ...
Full-text available
Mastication is the first step of food intake and digestion, which is a complex act that requires the participation of the whole functional stomatognathic system. Among all its components, teeth are regarded as the executor and take the main charge of mechanical food breakdown. Based on this, many researchers suspected that there is a mutual effect between malocclusion and masticatory function, and made efforts to figure out their interrelationships. Various studies have revealed that the alteration of occlusal patterns may decrease masticatory capability, and orthodontic treatment is an efficient solution. On the other hand, it is also detected by clinicians that masticatory anomalies can have a long-term impact on the formation of malocclusion and dentomaxillar deformities. But due to the complexity of stomatognathic system and the uncertainty of individual growth, this theory has not yet been sufficiently validated. Therefore, in order to define dental positions tuned to masticatory capabilities, it is necessary to understand how different associated factors including the properties of food textures, masticatory muscles, bite forces and chewing patterns affect dental positions and craniofacial growth in the long run. This review is determined to summarize the previous evidence and try to illustrate a potential rule so as to avoid postponed intervention and correction of subsequent masticatory dysfunction and malocclusion.
... While the concept of Ω is entirely new, its physical meaning may be linked to the micro-cracking mechanism in that upon applied strain the micro-cracks accumulate with time, promoting failure. Although long term fracture (lowε, high Ω) may not be relevant to the short time scales of oral breakdown (highε, low Ω) reported in the literature (Gerstner et al., 2010;Skamniotis et al., 2016;and Wang and Chen, 2017), yet it may concern the markedly longer time scales reported for the gastric breakdown (Kong and Singh, 2008;Arora et al., 2005;and Bilecen et al., 2000). Moreover, while oral breakdown can be considered to occur through monotonic loading by the teeth, the same cannot be deemed for gastric breakdown, where the mechanical force history applied on the bolus has not been adequately quantified yet (Vassallo et al., 1992 andCleary et al., 2015). ...
Mastication is responsible for food breakdown with the aid of saliva in order to form a cohesive viscous mass, known as the bolus. This influences the rate at which the ingested food nutrients are later absorbed into the body, which needs to be controlled to aid in epidemic health problems such as obesity, diabetes, and dyspepsia. The aim of our work is to understand and improve food oral breakdown efficiency in both human and pet foods through developing multi-scale models of oral and gastric processing. The latter has been a challenging task and the available technology may be still immature, as foods usually exhibit a complex viscous, compliant, and tough mechanical behaviour. These are all addressed here through establishing a novel material model calibrated through experiments on starch-based food. It includes a new criterion for the onset of material stiffness degradation, a law for the evolution of degradation governed by the true material's fracture toughness, and a constitutive stress-strain response, all three being a function of the stress state, i.e., compression, shear, and tension. The material model is used in a finite element analysis which reproduces accurately the food separation patterns under a large strain indentation test, which resembles the boundary conditions applied in chewing. The results lend weight to the new methodology as a powerful tool in understanding how different food structures breakdown and in optimising these structures via parametric analyses to satisfy specific chewing and digestion attributes. Published by AIP Publishing.
... One unique feature of mastication is its relatively invariant rhythm (Ekuni, Furuta, Takeuchi, Tomofuji, & Morita, 2012;Ross et al., 2007). It was long believed that chewing rate was linked to jaw mass or lever arm biomechanics (Druzinsky, 1993;Gerstner, Madhavan, & Braun, 2014;), but recent work suggests this is not the primary relationship (Carvalho & Gerstner, 2004;Gerstner, Cooper, & Helvie, 2010;Ross et al., 2017;Stover & Williams, 2011). Muscle mechanics and salivary flow rate have been proposed as playing roles (Virot, Ma, Clanet, & Jung, 2017). ...
Objective: Mastication consists of rhythmic jaw openings and closings. Recent studies suggest that muscle mechanical properties determine the rhythmic rate; however, speed-accuracy tradeoffs may also play a role. This study evaluated how variation in chewing rate affected chewing performance, how masticatory muscle activity varied with chewing rate, and whether morphology and demographics contributed to performance. Design: Chewing performance and muscle activity were sampled in 23 healthy, fully-dentate adults, who chewed a standardized test food to a metronome set at 0.5, 0.75, 1, 2 and 3 times their 'natural' chewing rates. Subjects produced ten chews per trial, and five trials for each of the five rates. Surface electromyographic (EMG) activity was sampled from masseter and temporalis muscles bilaterally. Demographic, occlusal, and cephalometric data were also obtained. Results: Chewing performance, defined by median particle size, was inversely related to chewing rate; however, performance was not remarkably improved at rates slower than the natural chewing rate. Above the natural chewing rate, variability in EMG bursts diminished, suggesting a reduction in muscle activity modulation at fast rates. Occlusal contacts and most morphological features appeared to play a limited or no role in performance. Conclusions: Results support the hypothesis that the 'natural' chewing rate is selected to be as fast as possible while providing sufficient time to allow EMG modulation for improved performance. The interplay between EMG modulation and individual variation in skeletal morphology is likely critical for optimal chewing performance.
... The y-displacement of 14 mm was applied at the constant speed of 50 mm s 21 followed by a recession of 8 mm at 145 mm s 21 . The bite cycle period was 0.34 s, which was based on biometric data for the boxer breed [51]. ...
The study of oral processing and specifically cutting of the food piece during mastication can lead towards optimization of products for humans or animals. Food materials are complex biocomposites with a highly nonlinear constitutive response. Their fracture properties have not been largely investigated,while the need for models capable of predicting food breakdown increases. In this study, the blade cutting and the essential work of fracture (EWF) methodologies assessed the fracture behaviour of starch-based pet food. Tensile tests revealed rate-dependent stiffness and stress softening effects, attributed to viscoplasticity and micro-cracking, respectively. Cutting data were collected for 5, 10 and 30 mm/s sample feed rates, whereas the EWF tests were conducted at 1.7, 3.3 and 8.3 mm/s crosshead speeds corresponding to average crack speeds of 4, 7 and 15 mm/s, respectively. A reasonable agreement was achieved between cutting and EWF, reporting 1.26, 1.78, 1.76 kJ/m2 and 1.52, 1.37, 1.45 kJ/m2 values, respectively, for the corresponding crack speeds. These toughness data were used in a novel numerical model simulating the ‘first’ bite mastication process. A viscoplastic material model is adopted for the food piece, combined with a damage law that enabled predicting fracture patterns in the product.
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Mammalian chewing rate scales inversely to body mass (M); however, controversy exists over the value of the scaling exponent. Different mechanisms explain different values of the scaling exponent; hence, a better estimate of the exponent would provide insight into the mechanisms governing chewing rate across mammalian species. We evaluated the relationship between mean chewing cycle duration (CD; i.e., the inverse of mean chewing rate) and M in 132 species and removed phylogenetic effects by using an independent contrast method currently used in evolutionary biology studies. A one-third-power law resulted when independent contrasts were not used; however, a one-third- to one-fourth-power law resulted when independent contrasts were used to remove phylogenetic effects. We hypothesize that variation in the scaling exponent is due to natural selection acting to increase metabolic efficiency; and variation in the complexity of mandibular kinematics, motor control asymmetry, and mandibular biomechanics, which may act to increase CDs above the ''ideal'' one-fourth-power law. Future studies should consider effects due to jaw-movement kinematics, motor control issues, and biomechanics.
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The study of motor control is one of the most active areas of research in feeding functional morphology. As many of the muscles responsible for controlling feeding behavior are large and superficial, it has proved relatively easy to record muscle activity during feeding using electromyographic techniques. As a result, feeding muscle activity patterns have been quantified for representatives of all the major vertebrate groups, although sampling intensity varies from relatively good in groups like fishes and amphibians to poor in sharks, turtles and snakes (see Schwenk, 2000; this volume for an overview). Conservation as a theme has shaped inquiry into feeding motor control. Ideas of motor pattern conservation in fish and other aquatic vertebrates grew out of early experiments on rapidly feeding fishes which were found to have stereotypical kinematic and muscle activity patterns ( e.g., Osse, 1969; Liem, 1970; Nyberg, 1971; Lauder, 1980). Central pattern generators (CPGs) were hypothesized to control rapid suction feeding, a behavior thought to occur so rapidly that it precluded sensory modulation (Osse, 1969; Liem, 1978; Groebecker and Pietsch, 1979; Groebecker, 1983; Liem, 1984). Although subsequent studies demonstrated that muscle activity patterns could vary in response to prey differences ( e.g., Wainwright and Lauder, 1986; Friel and Wainwright, 1998), the general hypothesis of motor pattern conservation was strengthened by a number of quantitative studies of muscle activity which showed that, at low phylogenetic levels, species motor patterns were statistically indistinguishable ( e.g., Sanderson, 1988; Wainwright et al. , 1989). The central pattern generator hypothesis provided a mechanism to explain both patterns of stereotypy ( e.g., Liem, 1978; Lauder, 1980) and conservation (see Smith, 1994) Observations of motor pattern conservation were congruent with the identification of many conserved features of biomechanical design in suction feeding fishes ( e.g., Lauder, 1985 …
It is clear from Andrew Lawler's article “DOE labs: Is evolution enough?” (News & Comment, [14 June, p. 1577][1]) that many of the U.S. Department of Energy (DOE) labs have lost their raison d'etre, and I am uncomfortable with the idea that the labs themselves are trying to find new missions in
The biomechanical determinants of the scaling of chew cycle duration are important components of models of primate feeding systems at all levels, from the neuromechanical to the ecological. Chew cycle durations were estimated in 35 species of primates and analyzed in conjunction with data on morphological variables of the feeding system estimating moment of inertia of the mandible and force production capacity of the chewing muscles. Data on scaling of primate chew cycle duration were compared with the predictions of simple pendulum and forced mass-spring system models of the feeding system. The gravity-driven pendulum model best predicts the observed cycle duration scaling but is rejected as biomechanically unrealistic. The forced mass-spring model predicts larger increases in chew cycle duration with size than observed, but provides reasonable predictions of cycle duration scaling. We hypothesize that intrinsic properties of the muscles predict spring-like behavior of the jaw elevator muscles during opening and fast close phases of the jaw cycle and that modulation of stiffness by the central nervous system leads to spring-like properties during the slow close/power stroke phase. Strepsirrhines show no predictable relationship between chew cycle duration and jaw length. Anthropoids have longer chew cycle durations than nonprimate mammals with similar mandible lengths, possibly due to their enlarged symphyses, which increase the moment of inertia of the mandible. Deviations from general scaling trends suggest that both scaling of the jaw muscles and the inertial properties of the mandible are important in determining the scaling of chew cycle duration in primates.
This review describes the patterns of mandibular movements that make up the whole sequence from ingestion to swallowing food, including the basic types of cycles and their phases. The roles of epithelial, periodontal, articular, and muscular receptors in the control of the movements are discussed. This is followed by a summary of our knowledge of the brain stem neurons that generate the basic pattern of mastication. It is suggested that the production of the rhythm, and of the opener and closer motoneuron bursts, are independent processes that are carried out by different groups of cells. After commenting on the relevant properties of the trigeminal and hypoglossal motoneurons, and of internuerons on the cortico-bulbar and reflex pathways, the way in which the pattern generating neurons modify sensory feedback is discussed.