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Integration of multi-level dental diversity links
macro-evolutionary patterns to ecological strategies
across sharks.
Roland Zimm1,*, Vit ´
oria Tobias-Santos2, and Nicolas Goudemand1
1Institut de G´
enomique Fonctionnelle de Lyon, Ecole Normale Sup ´
erieure de Lyon, CNRS UMR 5242, Universit´
e
Claude Bernard Lyon1, Lyon Cedex 07 69364, France
2Laboratoire de Biologie du Developpement de Villefranche-sur-Mer (LBDV, UMR 7009), Institut de la Mer de
Villefranche (IMEV), Sorbonne Universit´
e, CNRS, Villefranche-sur-Mer, France
*zimm.roland@gmail.com
ABSTRACT
Sharks are notorious for their exceptional dental diversity, which is frequently used as a proxy for ecological function. However,
functional inferences from morphology need to consider morphological features across different organizational scales, from
small cusplets to patterns at the level of the entire jaw. Here, we deploy a set of classic and novel morphometric approaches
to quantify morphological features ranging from sub-dental features to whole dentitions within a large ensemble of species
encompassing all extant orders of sharks. We then correlate these measures with habitat, feeding and body size traits and
track their variation as a function of genetic distance, a measure of trait adaptability. Intriguingly, sharks tend to either explore
tooth-level or dentition-level complexity, resulting in two distinct groups with key differences in tooth symmetry, graduality
of heterodont change, and depth of habitat. Overall, we find that intermediate levels of resolution, namely monognathic
heterodonty in comparison with dignathic heterodonty and tooth-level shape descriptors, show the strongest predictive power
for ecological traits, while exhibiting low phylogenetic signal, which suggests a more dynamic adaptability on shorter evolutionary
timescales. This raises macro-evolutionary interpretations about the evolvability of nested modular phenotypic structures, with
likely important implications for paleo-ecological inferences from sequentially homologous traits.
Introduction
Teeth have been used as a powerful proxy for ecological function and adaptive evolution across vertebrates
1–7
As a hyper-diverse
structure displaying considerable morphological change within short evolutionary frameworks
8–10
, tooth shape tends to be
fine-tuned for food acquisition and mastication strategies
6
. This is particularly important for the reconstruction and study of
past ecosystems where fossil teeth are often among the most abundant remnants within an inherently patchy and limited data
record, providing critical information about ecological niche occupancy
2,4,11–13
While complexity and form of isolated teeth
are informative about potential functions
14–17
, teeth tend to act together as a whole (or partial) dentition, forming an emergent
functional unit. Thus, single-tooth morphology is only one of several different organizational levels - spanning from sub-dental
features (e.g.serrations) to entire dentitions - that matter for specific functional aspects and their integration. This amounts to a
limitation in paleontological studies often relying on dispersed isolated teeth whose relative positions are deduced indirectly
18
.
The functional integration of teeth, as whole dentitions or by regional subfunctionalization, is further illustrated by the
widespread occurrence of heterodonty (juxtaposition of differently-shaped teeth). Like mammals, sharks exhibit conspicuous
tooth morphological variation at different scales, from single tooth to dentition levels. Interestingly, this is not a recently
evolved pattern, as heterodont arrangements of multicuspid teeth are described among the earliest sharks
19
.Albeit less studied
as mammals, shark odontogenesis shares many central features with the former class, involving conserved regulatory pathways
and developmental mechanisms
20–22
, besides notable differences
20,23
. Such deep similarities across long phylogenetic distances
point towards a kernel of developmental mechanisms capable of generating highly diverse dental morphologies both between
and within individuals, which has been explored experimentally and computationally
23–25
. While a system of tooth classes is
well-established in mammals
26,27
, systematic knowledge about heterodonty biases in sharks is sparse. For specific low-rank
taxonomic groups, patterns of tooth shape and size variation along the jaw are often diagnostic
28,29
. Some of these patterns have
been linked to feeding mechanics, emphasizing the importance of dentition-level perspectives when connecting morphology
and ecological functions
30
, while stark tooth morphology differences persist between sexes and age cohorts
29,31–34
. This is why
several studies have dissected dentitions morphometrically within proximate phylogenetic contexts31,33,35.
Complementarily, many studies describing shark tooth morphospaces have used isolated teeth across wider taxonomic
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levels, identifying clade- and ecotype-specific clusters and distributions
33,36–39
.Representing higher organizational levels, jaw
geometry, cranial shape, musculature and other anatomic macro-features have been connected to different feeding strategies
40–42
,
underlining the adaptive interplay of traits on different scales. This suggests that a class-wide analysis of functional associations
between dental variation and complexity at different scales and ecological functions might be highly informative. Here, we
elucidate such macro-patterns in the light of environmental and life-history traits, applying a novel combination of morphometric
tools. This may critically contribute to understanding which organizational level, across an entire vertebrate class, is most
relevant or predictive for functional traits, a significant question across ecology, paleontology and evolution.
Results
1. Heterodonty is widespread across sharks
Heterodonty reflects the degree of tooth shape variation within a given individual. Such variation is not always subtle and
gradual, limiting the use of common homology-based morphometrics tools (e.g.landmark and semi-landmark-based approaches),
arguably biasing efforts to focus on species with gradual heterodonty
29,31
and making class-level comparisons rare
36,37
. We
calculate different types of within-toothrow heterodonty, namely differences between neighbouring teeth within the same jaw
(sequential monognathic heterodonty, HMS), between all teeth within the same jaw (total monognatic heterodonty, HMT), and
between teeth from the same relative positions in opposing jaws (dignathic heterodonty, HDG) (cf.FIG.1). This pipeline allows
reducing complex and multidimensional features to one-dimensional measures, and comparing morphologically heterogeneous
taxa. Since heterodonty measures quantify shape variation between units, they can be considered a proxy for high-level
(i.e.jaw-level) complexity. We also devise different proxies for single-tooth morphological complexity based on 2D-outline
characteristics (see FIG.11), enabling us to contrast tooth and dentition-level features.
In order to assess the prevalence of heterodonty across sharks, we collected 2D shape information about complete or nearly
complete dentitions from 51 species, using an open data collection (J-elasmo
43
), representing all extant shark orders. The above
defined measures identified substantial levels of heterodonty within all major clades of sharks, albeit in varying degrees (FIG.2).
While no significant difference emerges between the two superorders Squalomorphii and Galeomorphii regarding total and
maximal monognathic heterodonty (P
HMT
=0.3386, P
HMTmax
=0.928, two-sided Wilcoxon test), sequential heterodonty is lower
(P
HMS
=0.0755), and dignathic heterodonty significantly higher (P
HDG
=0.0058) in Squalomorphii.Overall, these findings do not
generally support strong superorder-level biases that might facilitate, or canalize, dental variation within individuals.
2. Heterodonties are correlated.
It is conceivable that some species may show a high degree of between-jaws tooth variation without exhibiting high variation
between adjacent teeth, and vice versa. However, we find strong correlations between monognathic and dignathic heterodonties
(HMT~HMS: R=0.9197, HDG~HMT: R=0.4236, HDG~HMS: R=0.3062), suggesting that variation within and between
jaws is not independent (FIG.3); the main outliers being squalean, which, on average, exhibit low monognathic but high
dignathic levels of heterodonty (FIG.9A). The highest levels of both monognathic and dignathic heterodonty are found within
Hexanchidae featuring highly specialized dentitions, whereas Mustelus, Squatina, Nebrius and Squalus occupy the opposite
end of the distribution. Strikingly, the latter genera exhibit very different tooth morphologies, ranging from plaque-like
(M.mustelus), unicuspid (S.dumeril), to asymmetrically bent (S.mitsukurii) and complex, multicuspid, teeth (N.ferrugineus),
indicating that there may be no trivial correlation between tooth-level complexity and dentition-level complexity. This is
quantified by low-to-medium correlations between different complexity and heterodonty measures (HMS~Complexity: R=0.196,
HMT~Complexity: R=0.1275, HDG~Complexity: R=-0.063). Overall, most galeomorphs show a more gradual heterodonty
pattern than squalomorphs, as measured by the ratio of the maximal shape difference and HMS between any two teeth (FIG.9B).
3. Patterns of heterodonty and dental complexity across phylogenetic distances
The widespread occurrence of heterodonty and tooth-level complexity (cf.FIG.2) across the entire shark phylogeny suggests a
repeated evolution of these features. In order to test how dynamically these features vary at different phylogenetic scales, we
correlated genetic distances with differences in dental morphological descriptors. This analysis reveals that between genetically
close or moderately distant species, there is no significant positive correlation between genetic and heterodonty increases (FIG.4,
see also FIG.15). Only the genetically most distant species exhibit significantly higher heterodonty differences. This finding
implies that heterodonty can change relatively unconstrainedly, suggesting substantial evolvability. Conversely, differences in
tooth-level complexity increase significantly between very closely and intermediately related species. However, this does not
apply to every specific measure of tooth-level complexity individually, as differences in cuspidity are uncorrelated with genetic
distance (FIG.15). Overall, our findings suggest that some tooth-level complexity measures might serve as a moderately better
proxy for relatedness than heterodonty.
Despite the absence of a substantial mid-range phylogenetic signal for specific heterodonty measures, we tested whether
combinations of these measures differ between main shark clades. Using canonical correlation analysis (CCA), we are able to
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separate Squalomorphii and Galeomorphii, the two shark superorders (FIG.5). While including only morphological features
into the CCA allows to separate the Squalomorphii from 80% of Galeomorphii along the first canonical axis (FIG.5A), adding
some key ecological features leads to a complete separation of the two superorders (FIG.5B). Interestingly, the ratio between
monognathic and dignathic heterodonty, and graduality of heterodont change, appear to be better superorder separators than
each heterodonty measure in isolation (FIG.5C). Consistent with the higher correlation between differences in dental complexity
and genetic distance, we find that certain tooth-level complexity measures, such as fine cuspidity (and the ratio between fine and
coarse cuspidity) and Fourier-based complexity, show a significant clade-specific range of values. Finally, our study also reveals
significant clade-specific differences in ecological parameters, especially depth, suggesting that the identified heterodonty and
tooth complexity patterns may at least partly represent patterns of adaptive morphological changes.
4. Correlations between ecological traits and heterodonty
Since tooth shapes and their arrangement within dentitions are expected to be fine-tuned towards specific niches, we evaluated
correlations with ecological trait proxies. Specifically, we collected information on prey categories coarsely associated with
different trophic guilds or feeding strategies (cephalopods, other molluscs, crustaceans, unspecified worm-shaped animals,
non-osteichthyan vertebrates, trophic width, trophic level), habitat categories (shore, shelf, deep sea, open ocean, reefs, benthos
and marine nectos, depth and depth range), and body size. We used both linear correlation models and canonical variate
correlations between dental measures and ecological features (FIG.6). Although partially overlapping, these two approaches
yield distinct profiles, owing to different methodologies. Dignathic heterodonty shows some correlations with habitat classes,
whereas monognathic heterodonty measures emerge as important diagnostic predictors in the canonical analysis paradigm.
Intriguingly, we find stronger correlations between dental features and habitat than trophic categories. An inverse correlation
pattern separates residents of shallow and open-sea habitats, with species inhabiting the deep sea presenting another pattern of
correlation with heterodonty and tooth complexity traits (FIG.18). Across trophic guilds, differences appear between nectic
(vertebrates and cephalopods) versus bottom-dwelling prey classes (crustaceans, non-cephalopod molluscs and diverse small
invertebrates). Finally, coarse cuspidity is distinctive for the difference between bottom-vs.-water column feeding strategies,
actively hunted prey, and body size, while fine cuspidity is informative about size, and deep-vs.-shallow habitat distinction.
Taken together, we observe distinct correlation patterns between dental descriptors and ecological traits.
5. Two contrasting strategies emerge
In addition to phylogenetic and ecological groups, we find that shark species diverge along two disparate directions when
plotting monognathic heterodonty against Fourier complexity (FIG.7A). While the first group (G1) shows high Fourier tooth-
level complexity, but low monognathic complexity, the second one (G2) presents the reverse pattern. Teeth in G1 tend to be
smoother, more obtuse and asymmetric relative to G2. Significant differences emerge when comparing the ratios between
(a) coarser and finer cusp numbers, (b) different heterodonty measures, and (c) outline-vs.angle-based tooth similarity or
complexity measures (FIG.7C). Leveraging combinations of these measures reveals a specific shape pattern, with G1 featuring
asymmetric, compact teeth that vary little within jaws, and G2 featuring more excentric or triangular teeth that are part of
morphologically heterogeneous gradually changing dentitions. Interestingly, trophic differences between the groups are not
salient. However, species belonging to G1 tend to inhabit deeper regions, while G2 species are found closer to the surface,
including both proximal (shores) and distal (open ocean) environments (FIG.7B,C).
6. Monognathic heterodonty is the most relevant predictor for ecological traits.
As measures such as cuspidity quantify complexity on a single-tooth level (the unit), heterodonty refers to complexity resulting
from the higher-order composition of these units. Based on this realization, our dataset is suitable to analyze to which extent
complexity on each of those different scales matters ecologically. As a proxy for relevance, we compared correlation strengths
of CCA1 coefficients (cf.FIG.6), comprising different heterodonty and tooth-level shape descriptors used to separate sharks by
ecological traits or phylogenetic group, respectively. Globally, monognathic heterodonty shows particularly high correlation
strengths, while fine cuspidity, in particular, yields significantly lower values (FIG.8). In detail, we also observe high correlations
between food-related canonical variates and coarse cuspidity as well as dignathic heterodonty and depth, whereas body size is
the trait most significantly correlated with fine cuspidity. Overall, we conclude that intermediate complexity levels (namely
monognathic heterodonty) are the best predictors of most ecological traits.
Discussion
Although separated by over 400Ma of evolution44, both sharks and mammals show remarkable dental variation both between
and within individuals and species. We show quantitatively that, within sharks, this diversity is not restricted to specific clades
but evolves dynamically. This suggests conserved developmental mechanisms capable of producing a large range of potentially
adaptive tooth shapes
23,24
. Recent work has increased insight into shark odontogenesis and revealed commonalities with
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its mammalian counterpart
20,45
. However, important differences comprise depth of tissue embedding of tooth development,
continuous generation of new teeth germs resulting in deciduous tooth series (a similar feature is present in polyphyodont
mammals, too) and the local regulation of cell proliferation and apoptosis
20
. On the level of single-tooth morphologies, in
silico approaches to mammalian and shark odontogenesis implementing some of the described differences suggest a similar
capacity of generating phenotypic diversity
23,46
. However, on the level of dentitions, graduality differences between adjacent
teeth are expected. This is because in mammals, Hox genes play a key role in distinguishing different tooth types along the jaw,
which has not been shown yet in sharks26,27.
Our analysis reveals that Squalomorphii exhibit larger but less gradual variation between adjacent teeth than Galeomorphii.
We suggest that the former’s lower graduality is partly due to highly heterodont dentitions in the Hexanchidae that appear
to be functional and conserved throughout the proximate fossil record
47
, and to the cutting blade tooth rows widespread in
Squaliformes that depend functionally on interlocked homodont teeth, thereby reducing heterodont variation
48
. Although several
studies have documented gradual tooth shape variation in some galean species
29,31–34,39
, we are not aware of any previous
study comprehensively quantifying heterodonty throughout the entire class. Interestingly, jaw-level differences between
Squalomorphii and Galeomorphii may point to different feeding strategies that are reflected both by dentition features and jaw
shape
1,40
. Strong functional adaptive pressures may also underlie increased phenotypic evolution rates in Squalomorphii
40
.
While a comprehensive study analysing a single-tooth morphospace across sharks did not show clearly separated morphospace
regions for orders and superorders, morphospace occupancy of Squalomorphii and Galeomorphii presented a clear bias
36
. Yet,
whether development or functional constraints ultimately underlie these differences between the major clades remains to be
elucidated.
Between ecological features and heterodonty, correlations emerge indicating that quantitative shape differences between
teeth are functional. Specific correlations, however, may vary by method (one-to-one versus canonical correlation), highlighting
that dental adaptations to ecological niches are best described by a complex combination of features. The finding of stronger
correlations with habitat-related than trophic traits might prima facie contradict the role of teeth in food gathering and
processing. However, many sharks are opportunistic feeders, completeness of trophic information varies considerably between
species, and prey composition may change seasonally and between age cohorts (e.g.
49,50
). Habitats are usually correlated
with feeding habits and may serve as a coarser, yet more inclusive, proxy for a species’ primary food sources. Intriguingly,
we found striking similarities in heterodonty correlation patterns between specific habitat and food combinations, suggesting
discrete clusters representing major strategies: (1) shallow-water habitats and crustacean/small invertebrate diets, (2) open-sea
habitats, large preys, with a sub-cluster of deep-sea cephalopod feeders. It is tempting to interpret these clusters in terms of
different feeding mechanics
1
. Many shark species specialized on hunting larger vertebrates, which tend to populate in-shore
or ocean surface habitats, use their jaws in a saw-like manner
14–16,51
, mechanically benefiting from dental serrations (i.e.fine
cusps), absence of larger cusps, and low-to-intermediate heterodonty. Biomechanical studies showed that serrated teeth reduce
tear and shear stresses involved in dismembering large prey, while impeding puncturing, reducing suitability for smaller or
hard-shelled prey
14–16
. Interestingly, several squalean taxa exhibit dignathic heterodonty with interlocking asymmetric lower
teeth and simpler arrow-shaped upper teeth, which cooperate in a holding-sawing mechanism
1
. Consequently, increases
in dignathic heterodonty may have enabled the emergence of mechanistically complex feeding strategies especially across
Squalomorphii
41,48
. Many benthic feeders, including many deep-sea and reef dwellers, employ complex collecting-crushing or
ambushing-grasping strategies aiming at smaller, often hard-shelled, prey, in line with disparate tooth shapes and assemblies
along the jaw. Complementarily, feeding strategies often involve jaw-cartilage-level adaptations, indirectly affecting tooth
numbers and heterodonty gradients
41,52
. Increased monognathic heterodonty often reflects diversity of specialized functions
during different feeding stages. For instance, Heterodontus displays different tooth types, reflecting specialization in collecting
and crushing hard-shelled prey. Nevertheless, this atypical genus does not commonly stand out in our analyses, suggesting
persistent shark-wide trends in heterodonty. Conversely, many smaller reef-dwelling species catch small free-swimming animals
using multi-cuspid dentitions
41,42
. Taken together, habitat-heterodonty associations reflect how different environments and prey
guilds underlie the dynamic evolution of a finite set of functional feeding strategies with specific signatures both at the tooth
and dentition-levels.
We have described two different trends that can, most distinctly, be distinguished by Fourier (tooth-level) complexity
and total monognathic heterodonty (i.e.dentition-level complexity), complementarily. Thus, we conclude that species tend to
increase either tooth-level or dentition-level complexity, but rarely both of them. Thanks to their moderate association with
distinct habitats, these two groups help characterize and interpret key morphological aspects at different scales distinguishing
two mechanistically different feeding strategies. Feeding strategies of group one, associated with deep-sea and benthic habitats,
involve increased collecting and crushing of hard-shelled prey animals leveraging high individual tooth shape specialization
and complexity. Such specializations often involve adaptations of the entire feeding apparatus, i.e.modifications to jaw cartilage
shape, articulation and musculature, allowing for suction-based food acquisition mechanisms41,42, with possible implications
on dental shape. Conversely, the second group uses high-cuspid/serrated, and more homodont, dentitions to catch or dismember
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swimming prey. Such specialized adaptations require concerted fine-tuning on several levels, thereby strongly selecting against
intermediate phenotypes. On the single-tooth level, teeth adapted for specialized diets often occupy extreme positions on
morphospaces
38
, in line with this assumption. Thus, the discreteness of the two trends, visualized by divergence within the
morphospace, suggest morphological discreteness of highly specialized functional mechanisms. Our analysis shows that the
evolution of morphological functionality in a trait featuring repetitive structures, here exemplified by dentitions, involves
changes in complexity across scales. This is a common observation among "serially homologous" traits
53–55
, whose units share
developmental mechanisms that, during evolution, may accumulate divergent features ultimately leading to individualization.
Examples include limbs, vertebrae, and different ectodermal appendages across vertebrates. Interestingly, such organs often nest
repeated sub-structures at multiple levels, e.g.feather branches within feathers within plumage regions, or digits on limbs
56–59
.
Within dentitions, teeth are arranged in regular rows, but feature repeated structures themselves, namely cusps and smaller
cusplets, making them another great example of serially organized structures
60–62
. Differences between units often stem from
local differences in developmental regulation within the tissue background, or respective higher-level, structures
63–65
. This
is in line with morphometric analyses of specific shark clades or species, which display rather gradual patterns of dental
variation
28,29,31,37
, while only a few species, most conspicuously within Hexanchidae, show relatively discrete shape transitions.
Thus, by considering complexity across several nested levels of dental organization in sharks, this study is an important
extension from the common focus on a single level of morphological organization. In particular, our data allow to quantify and
compare the specific importance that each level of organization has with respect to function and adaptive evolution. Overall, we
see that monognathic heterodonties have a significantly stronger correlation with ecological traits than dignathic heterodonty
and cuspidity. A straightforward interpretation of these results is that specialized food processing types involve correlated
combinations of fine-tuned tooth shapes along the entire jaw, with function predicting overall arrangement of tooth shapes.
These realizations may be relevant for biomechanical studies, which often quantify single-tooth performances in piercing,
slicing or grinding
14–16
. In the light of our results, it may be important to complement those studies with whole-jaw testing
paradigms66, in line with the distinction of feeding types based on whole dentitions1.
Intriguingly, we do not find any significant correlation between low-to-moderate genetic distances and heterodonty
differences, implying absence of strong constraints that prevent closely related taxa from developing divergent heterodonty
patterns. A similar observation appears to apply to jaw shape differences, permitting stark divergences within relatively short
evolutionary timespans
40
. Conversely, disparity of tooth-level complexity, with the exception of cuspidities, appears to increase
with genetic distance, suggesting significant phylogenetic constraints. This suggests that adaptive change tends to involve
changes on the level of heterodonty rather than tooth morphology, possibly involving changes in developmental parameters
along the jaw.
Theoretical and experimental studies in mammals have demonstrated that both gradual and discrete heterodont tooth shape
change can be achieved by a gradual change of developmental parameters
24,67
, making tinkering with odontogenesis in a
global rather than local manner a suitable way of generating adaptive phenotypic change. We hypothesize that fine-tuning of
individual teeth without affecting their neighbours might be more difficult than altering jaw-level gradients of morphogens
or developmental factors, which will impact downstream odontogenesis locally. Additionally, studies considering evolution
on multiple traits have shown that even functionally intertwined traits such as upper and lower jaw in cichlids can exhibit
independent evolutionary dynamics
68
. In the context of shark dentition, this means that dignathic heterodonty should be
evolvable within short timespans, particularly in absence of occlusional constraints. This would render dentition an accessible
model of hierarchical developmental modularity underlying a mosaique fashion of evolutionary change in a set of functionally
or ontologically connected traits56.
Besides functional requirements and evolutionary contingency, differences in the frequency of variational patterns may
reflect developmental biases
69
. Given the weaker correlation of heterodonty differences with genetic distance compared to tooth-
level traits, and a stronger association of heterodonties and ecological specializations, it is tempting to speculate that evolutionary
change tends to developmentally originate from alterations in higher-level cues rather than from individually tinkering with
low-level features. Evidence for this hypothesis comes from different lines of research: Developmental studies have revealed
the explicit involvement of signal gradients from the jaw mesenchyme in establishing differences among mammalian teeth
65,70
.
Leveraging developmental transcriptomics, another recent study showed how evolution in one tooth will indirectly affect the
developmental regulation of teeth in other positions
71
. On the micro- to meso-evolutionary level, morphometric correlations
between different mammalian tooth types suggest regulation by shared, yet not identical, developmental factors
72
, although the
degree of covariation varies across traits and species73.
Developmental and ontological nestedness might be a major biological principle
56,74
. It has been argued that the repetitive
nesting of modules within generative networks can be considered a general principle of how to generate complex yet diverse
outcomes that transcends the domain of biology
75
. Theoretical research has emphasized that modularity will increase both
robustness to undesirable variation and evolvability
76–78
, while lines of evo-devo research have shown that functionally con-
nected modules can and often do evolve independently or at different rates
68,79,80
. In conclusion, teeth may be considered
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a model system to understand how nature adapts to environmental challenges not only by the emergence and fine-tuning of
hyper-diverse phenotypic modules
81
, but also by tinkering with their modular embedding in a morphological context over
different levels of organization.
Methods
Shark dentition data acquisition and processing
We extracted entire lateral tooth outlines from selected shark species published on the j-elasmo database (j-elasmo:
43
; outlines
downloaded 06-2022). This database contains displays of entire erupted toothrows of over 100 species. The selection for this
study was based on the criteria of phylogenetic representativity (i.e.sampling from all extant major clades and avoidance of
redundancy by sampling among phenotypically similar sister species) and completeness of dentition, aiming at high coverage
of all types of dentitions among extant shark species. Dentitions of which too many teeth overlapped visually were not used.
However, negligibly overlapping teeth, i.e. teeth whose partial overlap with adjacent teeth was minor and did not obstruct
important features such as cusps, were reconstructed by interpolation and comparison to neighboring teeth and included. More
substantially visually obstructed or damaged teeth were excluded from the analysis. Occasionally visible minor damages such
as small holes to the enameloid were manually corrected. The exception to the completeness criterion was Pseudotriakis
microdon which features extremely high counts of relatively small teeth. From this species, only a subset of teeth from different
jaw positions was used. In addition, we excluded planctivorous sharks due to their highly specialized dentitions. As sharks
keep generating teeth continuously, we defined toothrows as the contiguous sequences of fully erupted teeth along the jaw,
from meso/anterior to distal/posterior positions. Due to bilateral symmetry, only one jaw hemisphere was used. As the vast
majority of shark teeth are blade-shaped and only feature negligible morphological information in bucco-lingual direction, we
decided that 2D lateral views suffice for the purposes of our study. Tooth shape extraction was performed using custom-made
tools that automatically identify tooth boundaries and manual segmentation where necessary. Tooth size was not taken into
consideration within the scope of this study, because it cannot be explicitly included in the set of shape difference descriptors
we used. In other words, size differences can be considered a further, independent, dimension of phenotypic features that
may differ between teeth. Since the shape of the basal part of the tooth crown tends to show less morphological-functional
fine-tuning, than the upper part that is exposed to nutrition, and at times shows a less defined, undulating or porous transition to
the jaw mesenchyme, we decided to only consider the upper dental outline. In preliminary analyses for which the entire tooth
outlines were considered, we had found that morphometric patterns were in part driven by the basal rather than the upper part
of the tooth crowns. The segmentation point between upper and lower part of the outlines was defined by (1) a visual transition
of material, otherwise by (2) the most concave lateral point if a visible inflection could be discerned or (3) the most distant pair
of outline points in the lower part. Outline point numbers (1000 per tooth) were equalized by interpolation or data reduction in
order to ensure comparability across teeth.
Ecological information
In order to be able to associate tooth phenotypic information with potential ecological function, we collected proxy features
that were widely available in data bases and publications. The trophic level was estimated based on published information
from stomach contents or pre-calculated trophic scores as referenced in FISHBASE
82
, shark references
83
, and a number of
individual sources (see Sharks_eco_refs.xlsx for references
49,84–104
). In the few cases where suitable trophic information was
not available, the phylogenetically closest species for which sufficient records were accessible, were supplied instead. In the
occasional case of conflicting values, the more detailed, higher-quality, or more clearly documented of the available sources
was used preferentially. In addition, we assigned recorded prey items to larger trophic guilds. Piscivory was not assigned
as it is highly unspecific with respect to prey size and trophic level, and because nearly all species include fish into their
diet. Another specific diet, planctivory, was omitted, as the three plankton-feeding sharks feature very specialized dentitions,
which tend to occupy separate parts within morphospaces
37
. Body length, depth and habitat information was compiled using
FISHBASE
82
, Shark references
83
, and Sharks of the World
105
. Extreme body length values were neglected as exceptions,
or possibly overstated reports, and the documented ranges for mature male and female individuals were used. Unless noted
differently, length values used in our analyses were calculated as the average between upper and lower range limits for females
and males, respectively, and the average of those. Depth was annotated similarly, we averaged between the upper and lower
range limits that were reported, not considering exceptional reports. We also noted whether shark species were associated with
specific habitat types, as within the set of references used. For this assignment, we searched for habitat descriptor terms in
encyclopedic literature (as given above) that were not explicitly mentioned as exceptional occurrence. This way, we avoided the
need to define arbitrary limits between complementary ecological descriptors. Taken together, we used the following categories:
TROPH: average trophic level, VERT: non-osteichthyan vertebrate prey, CEPH: cephalopod prey, MOLL: other molluscan prey,
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CRUST: crustacean prey, VERM: further small usually worm-like invertebrate prey, OMNI: degree of omnivory or number
of documented food categories, SHORE: occurrence near shore line, SHELF: occurrence along the continental shelf zone,
REEF, reef habitat, OCEAN: open sea, DEEP: deep sea, BENT: benthic habitat, NECT: nectic habitat, DEPTH: general depth
of occurrence, D
LOW
: lower depth limit, D
HIGH
: upper depth limit, D
RANGE
: difference between D
LOW
and D
HIGH
, SIZE:
body length, as calculated above, S
MIN
, S
MAX
: reported extremes of adult/fertile individuals, S
HATCH
: size at hatching or birth.
Albeit potentially biased in multiple ways, this compilation of data represents what is currently known and available in the
published literature.
Phylogenetic analysis
To build the phylogenetic tree, the following, slowly evolving and commonly used genetic markers were selected: COI, cytB,
NADH2, the ribosomal 12S, 16S genes (with full sequences of 12S+tRNA-Val+16S where available) and rag-1. The choice was
made based on availability and a previously published phylogeny
106
. See the NCBI accession numbers in the Supplementary
table List_NCBI_refs.xlsx. The sequences were concatenated in the specified order and aligned with MUSCLE
107
(through
the program Unipro UGENE
108
) with default parameter options. Alignment regions with gap values higher than 95% were
trimmed. The phylogenetic tree was finally built using the program PHYLIP
109,110
, generating a neighbour-joining tree. The
neighbour-joining method was used herein because it aligns with standard approaches in comparable studies that involve similar
genetic datasets, allowing for direct comparability to previously published phylogenetic analyses, and because it provides
fast computation even for large sets of data and is appropriate for clustering the species relationships when the genetic data
is incomplete or heterogeneous. Kimura’s two-parameter model (K2P) was used to compute a distance matrix. We used a
boostrap of 1000, Seed values of 5 and the Majority Rule (extended) as a consensus-type choice were applied. The final tree
was visualized using TreeViewer
111
. For two species, corresponding genetic information was not available: Oxynotus centrina
and Heterodontus japonicus. In order to build the phylogenetic tree, O.centrina was added, integrating the phylogenetic analysis
of Straube et al.2015
112
, i.e.including the same relative branch lengths as provided therein, while H.japonicus was assumed to
have a position very close to H.zebra. Although both species belong to the same genus, we decided to include two specimens of
Heterodontidae to provide more than one data point for this clade. Where used, taxonomic categories were assigned according
to literature. However, commonly established taxonomic families that resulted in paraphyly were not assigned and polyphyletic
units were assigned independently. Intermediate taxonomic levels (super-families to infraorders) were determined based on the
phylogenetic tree at hand. Tree branch lengths normalized by maximal and minimal distance between species pairs were added
up to quantify genetic distances (dG).
Tooth comparison
In the absence of a methodological gold standard to quantify phenotypic similarity between tooth pairs, we devised a set of
six measures, thus capturing different aspects of shape, cf.FIG.1B. These similarity measures are then deployed to quantify
heterodonty by calculating average pair-wise distances between teeth according to the respective heterodonty definitions.
Partial Procrustes Alignment: To minimize the part of shape difference attributed to relative placement, we performed
an incremental shape rotation by up to +/-
π
/8, a shift along x and y axes by up to 10%, and a size change by up to +/-25%.
These ranges were determined as sufficient in precursory tests with a subset of shape comparisons. The initial size difference
correction was done in two ways, by normalization by total outline length, and normalization by total tooth area, and the lower
resulting distance was kept. Tooth area was defined by the outline and a straight line connecting its start and end points. Both
tooth outlines were centered on their centroids. For the first three shape distance measures, the Procrustes-aligned configuration
issuing the smallest distance between the outline pairs was then considered their definitive distance.
a) Euclidean mean distance (EMD): For each pair of morphologies, we calculated the mean distance between every point (x,y)
along one outline L1 and the physically closest outline point (X,Y) of the other morphology L2, irrespective of its relative
position. This procedure was conducted both-ways.
EM DL1,L2=∑nL1
i=1minnL2
j=1p(xi−Xj)2+ (yi−Yj)2
nL1
+∑nL2
j=1minnL1
i=1p(Xj−xi)2+ (Yj−yi)2
nL2
(1)
b) Homologous Euclidean outline distance (HED): while the EMD does not make any assumptions about homology, this
method compares identical relative positions along two outlines, thus representing pseudo-homology, with distant similarities
to semi-landmark methods. i = {1,..,nL1 } and j = {1,..,nL2} are the respective outline points in the two teeth.
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HE DL1,L2=∑nL1
i=1p(xi−Xk)2+ (yi−Yk)2
nL1
+∑nL2
j=1p(Xj−xl)2+ (Yj−yl)2
nL2
;k=jmin|i/ni−j/n j|;l=imin|i/ni−j/n j|(2)
For our specific purposes, nL1=nL2 =n, leading to a simplified formula:
HE DL1,L2=2∑n
i=1p(xi−Xi)2+ (yi−Yi)2
n(3)
c) Superimposed area overlap (SAO): This method calculates the ratio between the counts of overlapping and non-overlapping
parts of the overlaid tooth shapes. The lower boundary delineating the area is defined by a straight line connecting the start and
end points of the outlines. To calculate area overlap, both shapes were rasterized into a number of small squares S (i.e.pixels).
Before applying Partial Procrustes Alignment, the maximum x and y distances were used to discretize both axes into 100 units,
respectively.
SAOL1,L2=2∑SL1∩L2
∑SL1+∑SL2(4)
d) Discrete Cosine Fourier distance (DFD): Similar shapes are expected to be defined by sets of similar Fourier coefficients.
For this measure, we applied discrete cosine Fourier transformation on the tooth outlines, which incrementally approximates
semi-outlines by superimposing cosine lines. Distance is then calculated as the Euclidean distance between all coefficients (for
the first 24 harmonics)
Morphological distances between two shapes i and j were quantified by calculating Euclidian distances between the values
z(ε) of the Fourier coefficients ε,nεbeing the number of coefficients at 24 harmonics:
DFDL1,L2=snε
∑
ε=1zL1(ε)−zL2(ε)2(5)
e) Outline angle sum distance (OAD): Outline angles can be calculated between triplets of subsequential outline points. We
used the sum function of surface angles for n=100 equidistant outline points (i.e.after point number reduction in order to reduce
noise) as a descriptor of shape. We then overlaid the outline angle sum functions
a f (i)
of two tooth outlines L1 and L2, starting
from the same value, and calculated the area between them. Pairs of similar teeth are expected to show similar functions
a f (i)
and low differences in between.
OADL1,L2=Zn
0
|a f (i)L2−a f (i)L1|di (6)
f) Angle Function Discrete Cosine Fourier distance (ADD): In analogy to the DFD, we approximate the specific angle sum
function of the OAD by means of discrete cosine Fourier transformation. We then use the Euclidean distance between the
resulting Fourier coefficients to describe differences between a given pair of outline functions.
ADDL1,L2=snε
∑
ε=1a fL1(ε)−a fL2(ε)2(7)
Heterodonty measures
Heterodonty per dentition was then calculated as the average of all distances, as defined above, between any pair of teeth in
consideration. We distinguished between three different heterodonty measures: (a) Sequential monognathic heterodonty (HMS):
shape difference between neighboring teeth, (b) total monognathic heterodonty (HMT): shape difference between any pair of
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teeth within the same jaw, (c) dignathic heterodonty (HDG): shape difference between pairs of teeth at approximately opposite
positions on upper and lower jaws. Where the number of teeth differed between the opposing jaws, relative positions were
used, eventually causing the same tooth being compared to more than one tooth in the opposing jaw if the latter harbored a
larger number of teeth. A schematic of these measures can be seen in FIG.1A. Note that in sharks, no dental occlusion occurs,
allowing a higher degree of morphological freedom than in many mammals. In addition, we also recorded the maximum
distance between any two teeth within a given jaw (HMX). As a jaw-level descriptor, measures were normalized by division by
the respective number of tooth comparisons.
In the following measures, i and j refer to teeth within a respective toothrow or opposing toothrows for the case of dignathic
heterodonty.
HMS =∑n
i=1|xi−xi−1|+|xi−xi+1|
2n(8)
HMT =∑n
i=1∑n
j=1|xi−xj|
n2(9)
HDG =∑ni
i=1|xi−xk|
ni +∑n j
j=1|xi−xk|
n j ;jawk,j=jawi,l;k=jmin|i/ni−j/n j|;l=imin|i/ni−j/n j |(10)
HMX =max |xi−xj|;jawi=jaw j(11)
Unless declared otherwise, we calculated the values of heterodonty as the average of all six distance measures devised, in
order to minimize potential biases introduced by the choice of a specific method. For inter-species comparison, all values were
normalized by the global maxima and minima, respectively.
Tooth complexity measurements
As for shape distances between pairs of teeth, there is no commonly accepted gold standard method to quantify complexity,
even more as it may refer to different features. This is why we devised a range of different methods, as schematically shown in
FIG.11. For several analyses, we pooled similar complexity methods, such as outline-based, or angle-based methods. Where
not stated otherwise, we calculated total complexity as the sum of all introduced measures. In the displayed formulae, i and j
denote outline points of the same tooth, unless explicited otherwise.
(a) Coarse-grained cuspidity (CUSP1): the number of larger cusps.
(b) Fine-grained cuspidity (CUSP2): the number of minor cusplets. The difference to the previous measure was defined,
unavoidably, by an arbitrarily chosen relative size threshold: while the largest cusp was always considered major, cusps were
considered minor if their higher col was below 2% of the total length or if they clearly constituted a serration pattern on larger
cusps.
(c) Outline-to-area ratio (OAR): The total length of the outline was divided by the total area.
(d) Outline-to-centroid size ratio (OCR): instead of area, outline length was divided by the centroid size.
OCR =∑n
i=2p(xi−xi−1)2+ (yi−yi−1)2
q∑n
i=1p(xi−x)2+ (yi−y)2
(12)
(e) Outer/inner circle ratio (OIR): the area of the largest circle inscribed in the outline was divided by the area of the smallest
escribed circle encompassing the tooth outline. To prevent a few large ratios from skewing the distribution, we defined a cutoff
of 25. This measure captures differences in excentricity.
(f) Discrete Cosine Fourier coefficients sum (DFS): due to the definition of Fourier analyses, the size of its coefficients correlates
with the excentricity, feature diversity, and difference to a simple round shape. As such, the total sum of Fourier coefficients z
for a given outline can be used as a proxy of information required to describe shapes, i.e. shape complexity.
DFS =
nε
∑
ε=1
4
∑
i=1
|ziε|;i:coe f f icients ;ε:harmonics (13)
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(g) Angle sum (ANS): sum of all surface angles (here defined by three points along the outline; angles different from 180
◦
/
π
rad yield higher values.) for NR different resolutions R (defined by the total numbers of equally spaced outline points n
R
). For
our angle-based measures, we used six different resolutions with nR={8, 16, 32, 64, 125, 250} .
ANS =
NR
∑
R=1
A(R)∗(NR)−1;A(R) =
nR−1
∑
i=2
|∡xR(i−1)|xR(i)|xR(i+1)−π|(14)
(h) Angle sum cadence (ASC): a measure of difference between the angle counts across different resolutions. This reflects the
fact that repetitive traits will feature large differences between resolutions, while outlines with differently sized traits will show
less difference. In general, the latter case will be considered less complex, as it contains less information.
ASC =
NR
∑
R=2A(R)−A(R−1)(NR −1)−1(15)
(i) Angle disparity (AND): In a similar vein, we measure, for different resolutions, the diversity of angles between pairs of
adjacent points on the outlines. Larger diversity is associated with morphological complexity.
AND =
NR
∑
R=1
nR−1
∑
i=2
nR−1
∑
j=2
α(R,i)×α(R,j)(NR)−1(nR−2)−2;α(R,i) = ∡xR(i−1)|xR(i)|xR(i+1)(16)
(j) Orientation patch count (OPC)
7
: we count the number of contiguous outline streaks that are delimited by a change in absolute
direction. Absolute direction is defined by the vectors between subsequent outline points and discretized to absolute partitions
of a circle, i.e. a change of direction would correspond to a change of partition and an increase of the count. For this measure,
we used different partitions (2,4,8), different rotations of the coordinate system (no rotation, rotation by half a partition and by
quarter partitions for the lowest partition number) and the different outline resolutions listed above.
Phenotypic distance between species
In addition, we calculated total phenotypic distances DP between species pairs (i,j). This is to serve as a test to see how overall
similarity would scale with genetic distance, as calculated above. For this measure, teeth of comparable relative jaw positions
in two species were compared in a manner analogous to the dignathic heterodonty measure. ni and nj are the total number of
teeth per row for the two species, respectively.
DP(i,j) = ∑ni
i=1|xi−xk|
ni +∑n j
j=1|xi−xk|
n j ;jawk,j=jawi,l;k=jmin|i/ni−j/n j|;l=imin|i/ni−j/n j |(17)
Statistical analyses
Tooth mean shapes were calculated by averaging the discrete cosine Fourier coefficients within the set of chosen specimens
and inversely reconstructing the tooth shape. These operations were performed using the dfourier function contained within
the R package Momocs
113
. This package was also used to perform shape-based PCA. Canonical correlation analysis based
on varying sets of traits was conducted using the R package Cancor. In order to quantify the phylogenetic signal, we took
advantage of several of the most frequently used methods: Abouheif’s c-mean, Moran’s P, Pagel’s Lambda, Blomberg’s K. We
used available R packages to conduct the analyses: abouheif.moran, moran.idx from the adephylo library and phylosig from the
phytools library. We used common R functions (cor, t.test, wilcox.test) as well as the linear regression function via STATS of
gnuplot in order to calculate correlation coefficients and p-values.
Data Visualizations
We used gnuplot (version 5.2) and R (plot and ggplot2 functions) to plot data.
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upper jaw
lower jaw
Chlamydoselachus anguineus
upper jaw
lower jaw
Anterior/Mesial Posterior/Distal
Monognathic,
sequential
Monognathic,
total (all vs.all)
Dignathic
Heterodonties
α1
α4
α3
α9
α6
α8
α2
α5
α7
PCA1
PCA2
PCA1
PCA2
A
B
i
ii
iii
iv
v
vi
Etmopterus molleri
HMT
HDG
HMS
Figure 1. Overview of heterodonty measures. A) Different types of heterodonty: Heterodonty, a dentition-level disparity
measure, can refer to (1) average differences between successive teeth: HMS, (2) differences between pairs of teeth belonging
to opposing jaws, also termed dignathic heterodonty: HDG, (3) differences between all teeth within the same jaw: HMT. Here,
these different measures are exemplified using differently dashed arrows. While the upper dentition of C.anguineus shows
fairly similar teeth between upper (blue) and lower(red) jaw, the lower dentition of E.molleri displays conspicuous dignathic
heterodonty. B) Comparing tooth similarity: Lacking a universally accepted metric of phenotypic difference, we deployed 6
different measures to quantify differences between two given tooth outlines: (i) Euclidean mean distance; the average distance
between equally distant points along the tooth outline and the closest points of the second outline, respectively. Note that the
measure is only shown for distances from given points on the red onto the blue outline, albeit being actually applied both ways.
(ii) Homologous outline distance: we average pairwise distances between a sequence of equally spaced points on the two
outlines, i.e. we compare points with the same numbers/indices. (iii) Superimposed area overlap: similarity is calculated as the
ratio between overlapping to non-overlapping area. (iv) Discrete Cosine Fourier distance: The discrete cosine Fourier
describing outline shapes yields a number of coefficients defining a sequence of cosine functions with increasing harmonics.
Euclidean distances between corresponding coefficients are used to quantify shape distance. (v) Outline angle distance:
Assuming that surface angles reflect relevant outline features, we calculate the distance between the functions summing outline
angles at an intermediate resolution of outline points (i.e.for 100 outline points each) for two outlines. (vi) Angle Function
Discrete Cosine Fourier: We apply the discrete cosine Fourier as in (iv) on the functions used in (v), in an analogous manner.
Note that superpositions of the tooth outlines required for (i-iii) are anteceded by a partial Procrustes alignment.
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8
7
5
3
1
2
4
6
Trophic level
Complexity
Cx_Fourier
Cuspidity1
Cuspidity2
HMS
HMT
HDG
HMTx
1
0
1 - Hexanchiformes
2 - Squatiniformes
3 - Pristiophoriformes
4 - Squaliformes
5 - Heterodontiformes
6 - Orectolobiformes
7 - Lamniformes
8 - Carcharhiniformes
Orders
Trophic level
Galeomorphii Squalomorphii
Figure 2. Heterodonty is widespread across all shark clades. We selected 51 species across the entire Selachimorpha,
representing most of the extant shark diversity. Displayed branch lengths are proportional to genetic distance (see Methods) and
taxonomic orders are distinguished by background (and font) colour. The adjacent heatmap shows species-wise measures of
trophic level, tooth-level complexity (average of different measures, see FIG.11), Fourier-based tooth-level complexity,
cuspidities (1:coarse, 2:fine), and heterodonty measures, each normalized by the respective global minima and maxima:
HMS-sequential monognathic heterodonty, HMT-total monognathic heterodonty, HMTx-maximal heterodonty between any
two teeth of the same jaw, HDG-dignathic heterodonty.
r = 0.9197
r = 0.3062 r = 0.4236
dignathic Heterodonty (HDG)
dignathic Heterodonty (HDG)
total monognathic Heterodonty (HMT)
sequential Heterodonty (HMS) sequential Heterodonty (HMS)
total monognathic Heterodonty (HMT)
1
5
5
5
1
5
11
1
35
3
3
3
3
5
3
1
p-val < 2.2e-16 p-val = 0.011 p-val = 0.000602
Figure 3. Heterodonty measures are correlated. Sequential (HMS) and total (HMT) monognathic heterodonties, as well as
dignathic heterodonty (HDG) are plotted against each other, revealing positive correlations. Colors encode phylogenetic clades
(blue: Squalomorphii, red: Galeomorphii). P-values are based on Pearson’s correlation test.
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Rank dG
dG
Rank dG
dG
0
-1
-2
-3
-4
-5
0
-1
-2
-3
-4
-5
log10 (p-value)
dG ( normalized genetic distance )
HMT
HDG
Phenotype
Complexity
Cx_Fourier
family
taxonomic level
superorder
order
Rank dG
dG
A
BC
dG ( normalized genetic distance )
dG
negligible difference
p >> 0.1
dG
significant increase
p << 0.1
log10 (p-value)
Significance of change in feature
Significance of change in feature
Feature
Figure 4. Differences in heterodonty show no significant increase with genetic distance for low- to intermediate
taxonomic levels, unlike tooth-level complexity. We ordered all pairs of species by normalized genetic distance (dG) and
calculated the p-values (one-sided Wilcoxon test) for significance of difference of overall tooth-level complexity (orange),
Fourier-based complexity (red), total monognathic heterodonty (HMT, blue), dignathic heterodonty (HDG, dark blue) and total
phenotypic distance (grey) between two subsets of 100 species pairs each. Total phenotypic distance is based on position-wise
tooth shape comparisons between different species. Subsets were defined as containing the nth to the n+100th species pair
ordered by dG, for sliding (incrementally increasing) n. The two subsets were A) subsequent or B) 200 ranks apart, in order to
account for different scales of comparison. Here, the lines connecting the p-values are Bezier-smoothened and plotted against
dG of the highest member of the lower set of species pairs. A dotted line marks the 0.05-level of statistical significance. Inlets
show the relationship between dG and ordered ranks and examples of two pairs of subsets (higher:red, lower:blue). For
illustrative purposes, schematic examples of two pairs of set with low (red) and high (blue) p-values are displayed besides. C)
For orientation, we display the taxonomic compositions of the ordered species pair sets, with black representing the portion of
pairs from the same family (only few), light grey representing pairs from the same superorder, and white pairs stemming from
different superorders, with intermediate shades of grey referring to intermediate taxonomic levels.
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Heterodontiformes
Orectolobiformes
Lamniformes
Carcharhiniformes
Galeomorphii
Hexanchiformes
Squatiniformes
Pristiophoriformes
Squaliformes
Squalomorphii
CCA1
CCA2
r = 0.8071
Gam
Sqm
A
CCA2
r = 0.887
+Size
+Depth
+Trophic level
Gam
Sqm
B
CCA1
C
Cx combined
Sqm Gam
HMS
avg.depth
HDG
log(Cusp2)
HMX
HMT
Cx Fourier
log(Cusp2/Cusp1) HMT/HDG
log(Cusp1)
HMX/HMS
Sqm Gam
Sqm Gam
Sqm Gam Sqm Gam
Sqm Gam
0.076 0.93 0.34 0.0058 0.0005
0.7 0.0023 0.29 0.0045 1.7e-6
0.00077
0.00067
**
**
***
**
*
**
*
**
*
**
o
Figure 5. Heterodonty and tooth-level complexity measures separate shark superorders. A) Canonical correlation
analysis (CCA) reveals combinations of heterodonty and tooth-level complexity measures that are specific for the two
superorders, squalean (Sqm, turquoise) and galean (Gam, red) sharks. B) The two main clades are separated more clearly if
ecological traits are included into the canonical analysis. The colors of the displayed species acronyms correspond to the
respective orders, as displayed besides. C) Violin plots contrast specific features and feature combinations in Squalomorphii
and Galeomorphii, with p-values plotted above (Wilcoxon test). Monognathic and dignathic heterodonty, the ratio between the
two, Fourier-based tooth-level complexity (Cx_Fourier), and heterodonty and cusp ratios, as well as depth, show significant
differences between the two clades. Cx_combined is the sum of all tooth-level complexity measures, Cusp1 and Cusp2 are
coarse and fine cuspidity, respectively. Significance: 0.1>p>0.05: ° , 0.05>p>0.01: * , 0.01>p>0.001 : ** , 0.001<p : *** .
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superorders (phylo)
Trophic level TL (troph)
average body length (size)
average depth
lower depth limit (d_low)
higher depth limit (d_high)
large vertebrates (vert)
small invertebrates (verm)
cephalopds (ceph)
crustaceans (crust)
non-ceph. molluscs (moll)
shore
reef
continental shelf
open ocean
deep sea
benthos
nectos
predictors CCA
CCA correlation strength
linear correlation
Cx.exc
Cx.four
Cx.ang
HMS
HMT
HMX
HDG
Cusp1
Cusp2
Cx.exc
Cx.four
Cx.ang
HMS
HMT
HMX
HDG
Cusp1
Cusp2
AB
Pearson's R
Figure 6. Overview of correlations between heterodonty/complexity measures and ecological traits. A) Different
heterodonty measures, and measures of tooth-level complexity, show specific correlations with ecological features, such as
body size, depth, prey guilds and habitats. Intensities of red and turquoise display Pearson’s correlation coefficient R. B)
Analogously, canonical correlation analysis reveals that monognathic heterodonties are central in statistically separating
different habitats and trophic traits. Red and blue hues indicate correlation strengths of CCA1 for linear combinations of the
predictors, i.e. tooth level complexity and heterodonty measures (red: negative correlations; blue: positive correlations).
Cx.exec: comprises tooth-level complexity measures based on excentricity (OCR, OAR, OIR), Cx.four: Fourier-based
complexity (DFS) and Cx.ang: angle-based complexity measures (ANS, ASC, AND, OPC).
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1
0.5
0.5
0.25
Cx(Fourier)
HMS
B
AG1 G2
log(avg depth)
HMT
HMX / HMS
log(size)
Troph
log(Cusp2)
HDGang/HDGoutl
log(Cusp2/Cusp1)
HMT / HDG
Complexity
Cx(OIR)
HDG
HMTang/HMToutl
C
G1 G2 G1 G2
G1 G2
G1 G2
Cx.exc/Cx.ang
trophic habitat
g1 > g2
g2 > g1
1
0.8
0.6
1.4
1.2
vert
verm
ceph
crust
moll
shore
reef
shelf
ocean
deep
bent
nekt
p=0.076
p=0.175
0.013 0.0002 2.8e-8 0.19
0.53 0.92 0.00042 0.044
0.58
1.4e-7
0.0039
0.81
0.56
log(Cusp1)
0.08
G1 G2
0.01
*
*
*
***
** ***
*** o
* **
Figure 7. Combinations of heterodonty and tooth-level complexity measures reveal two distinct strategies. A) Many
shark species show, roughly, either high Fourier-based complexity and low monognathic heterodonty, or vice versa, but rarely
exhibit high values for both. Teal marks the former (G1), red the latter group (G2). Shown outlines display, respectively, mean
tooth shapes for both groups. B) Group-wise enrichments of trophic and habitat features, each ring presents the ratio between
the respective percentages of species belonging to G1 divided by the ones belonging to G2 and the expected unbiased ratio.
Ring size reflects the number of species per ecological category. p-values (Student’s t-test) are annotated for the most
significant differences. C) Violin plots visualizing further group-specific characteristics with p-values (Wilcoxon test). Notably,
the groups show divergent ratios between heterodonty measures based on outlines (Xoutl) and outline angles (Xang ),
corresponding to the heterodonty measures EMD, HED and SAO vs. OAD and ADD (cf.Methods). Cx.exc comprises
complexity measures based on excentricity (OCR, OAR, OIR), Cx.ang (ANS, ASC, AND, OPC) measures based on angle
complexity. Cx(OIR) is the minimal ratio between the areas of inscribed and escribed circles. Significance: 0.1>p>0.05: ° ,
0.05>p>0.01: * , 0.01>p>0.001 : ** , 0.001<p : *** .
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troph
habitat
size
depth
combined
fine-grained coarse
Cusp2 Cusp1
HMS
HMT
t.test
0.00041 0.000415
0.082
0.00154
Canonical correlation strength
HDG
Figure 8. Ecological relevance of dental shape descriptors varies across resolution levels. Box plots summarizing
canonical correlation strengths between different cuspidity and heterodonty measures, and heterogeneous ecological traits.
Correlation strengths were found highest for monognathic heterodonty, while fine cuspidity yielded the lowest average
correlation. As those measures represent, roughly, morphological trait differences (or complexity) on different relative
resolution levels from fine cusps to differences between jaws, they are plotted in ascending order from the finest to coarsest
scale. Canonical correlation strength can be used as proxy for average relevance, suggesting size scale-dependent differences in
ecological trait importance (cf.FIG.6). Colors separate features, while the grey boxes contain the combination of all traits.
Displayed p-values were calculated using a Student’s t-test. Underlying grey shapes are included for illustrative guidance.
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1
HMX/HMS
HMT/HDG
Heterodontiformes
Orectolobiformes
Lamniformes
Carcharhiniformes
Hexanchiformes
Squatiniformes
Pristiophoriformes
Squaliformes
3
51
AB
Figure 9. Heterodonty ratios exhibit differences across shark phylogeny. Ratios between monognathic and dignathic
heterodonty (HMT/HDG) and between maximal monognathic heterodonty and sequential monognathic heterodonty
(HMX/HMS, a proxy for graduality, with lower values indicating higher graduality), show different values between shark
orders. Here, shark orders are lined up along the y-axis and are marked by colours; the x-axes denote heterodonty ratios using
log scales. Notably, Squaliformes exhibit high dignatic and low monognathic heterodonty, while galean sharks show overall
more gradual shape change than squalean sharks.
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14 16
'./pca_density.log' us ($1==2&&$2==4?$3:NaN):4:5
0
20
40
60
80
100
120
140
PC4
PC2
35.2%
4.59%
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14 16
'./pca_density.log' us ($1==1&&$2==2?$3:NaN):4:5
0
20
40
60
80
100
120
140
PC1
39.7%
PC2
35.2%
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10 12 14 16
'./pca_density.log' us ($1==2&&$2==3?$3:NaN):4:5
0
20
40
60
80
100
120
140
PC2
PC3
35.2%
7.52%
Figure 10. Single tooth discrete Fourier PCA. We compared individual tooth shapes taking advantage of the discrete cosine
Fourier analysis. Here, we show calculated theoretical outlines for combinations of the first four PCs superimposed over binned
densities of their respective occupancies across all shark teeth used in the analysis. Higher densities correspond to darker 2D
bin colour.
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1 2 345 6 78
α
n partitions
n=2,4,8
12
3
4
1x 4x
3x 6x
k rotations
α
α
Disparity low
α
1n
1n
1n
α
1n
α
1n
OCR (Cx.exc)
OAR (Cx.exc)
OIR (Cx.exc)
DFS (Cx.four)
ANS (Cx.ang)
AND (Cx.ang)
OPC (Cx.ang)
ASC (Cx.ang)
Example tooth morphologies
high values low values
I) OCR II) OAR III) OIR
Outline-Centroid size Ratio Outline-Area Ratio Outer / Inner sphere Ratio
Discrete Fourier coefficient Sum
Angle Sum Angle Sum Cadence Angle Disparity
Orientation Patch Count
α
Disparity high
α
IV) DFS
VI) ANS VII) ASC VIII) AND
V) OPC
Complexity Measures Overview
A
B
principal components / harmonics
outline points
outline points
Cx.exc
Cx.four
Cx.ang
Figure 11. Overview of tooth-level complexity measures. A) Schematics illustrating the different methods. Different
background shades separate different groups of measures that tend to be correlated FIG.12: Cx.exc, Cx.four, Cx.ang. i) OCR:
ratio between outline length and centroid size. ii) OAR: ratio between outline length and total area. For these methods, high
complexity is associated with excentric shapes with large cusps or protrusions. iii) OIR: ratio between area of incircle and
excircle. Excentric and gracile shapes yield high complexities. iv) DFS: describing more complex and excentric shapes requires
more Fourier harmonics. In the example morphospace, more central shapes featuring smaller coefficient values, are more
similar to a circle. Thus, this measure does not attribute high values to reiterative patterns, such as equal cusps. v) OPC: outline
angles are discretized according to an absolute coordinate system with different partition numbers. Frequent change of discrete
angle numbers along the outline is associated with complexity of outline features. vi) ANS: sum of angles between outline
points for a set of resolutions (here exemplified with different colors), attributing complexity to outline feature density. vii)
ASC: this measure quantifies the difference of outline angle sums between different resolutions, i.e. it yields high values if
different outline features exist on different scales. viii) AND: diversity of outline angles, i.e. feature diversity. B) For each
complexity measure, examples of tooth shapes with high and low values are displayed.
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Correlations between heterodonty measures
Correlations between complexity measures
HMS HMT
correlation R2
correlation R2
correlation R2
A
B
HED
SAO
DFD
OAD
ADD
EMD
HED
SAO
DFD
OAD
EMD
HED
SAO
DFD
OAD
OCR
OAR
OIR
DFS
ANS
AND
OPC
ASC
Cusp1
OAR
OIR
DFS
ANS
AND
OPC
ASC
Cusp1
Cusp2
Figure 12. Correlations between measures. A) Species-wise correlations between heterodonty measures for sequential (left)
and total monognathic heterodonty (right). Outline similarity measures (EMD, HED, SAO) and, especially, angle-based
measures (ADD, OAD), show high internal correlations. B): Pair-wise correlations for all tooth-level complexity measures,
based on data for all teeth. Background shades of grey correspond to defined groups of measures (cf.FIG.11). Strong
correlations exist particularly between angle-based measures, while the Fourier-based method appears only weakly correlated
with the remainder, justifying the assumption of complementarity of methods. For all plots, linear correlation R values are
represented by greyscale.
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Cusp1 Complexity Rank
Cusp2 Complexity Rank
Order Order
Average (Cx.exc,Cx.four,Cx.ang) Complexity Rank
Average Rank Difference
A
B
Figure 13. Tooth-level complexity measures are complementary. A) Species are ranked based on coarse (Cusp1) and fine
(Cusp2) cuspidity. Colour corresponds to taxonomic order, in the same sequence as in 2. While both cuspidity ranks appear to
be generally correlated, some galean species show high fine cusp numbers (typically: serrations) while maintaining an overall
low coarse cuspidity. B) The average tooth-level complexity (based on a combination of Cx.exc, Cx.four and Cx.ang) is plotted
against the average rank difference when ranks for individual complexity measures are compared, one by one. Several species
show substantial differences in complexity depending on the measure applied, again suggesting complementarity of methods.
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y = 62.33 x + 115.6
r = 0.2927
phenotypic distance
relative genetic distance
phenotypic distance
Genetic distance binned
A
B
1 2 3 4 5 6 7
Figure 14. Phenotypic distance increases steadily with genetic distance. We correlate genetic distance and phenotypic
distance for all pairs of shark species. Phenotypic distance is calculated as the normalized Euclidean distance of Fourier
coefficients between pairs of teeth in corresponding relative positions along the jaw. A) correlation plot with linear regression
line. B) Genetic distances between pairs of species were binned by deciles, collapsing the first two and highest three deciles,
respectively, owing to data scarcity. Overall, a steady and almost monotonous increase of phenotypic distance with genotypic
distance is observed.
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cu1
mx
dg
cu2
Complexity Fourier Complexity
Cusp1 Cusp2
HMX HDG
HMS HMT
CX_complete
2
6
5
7
3
4
3
4
1
2
6
5
CX_Fourier
2
6
5
7
3
4
3
4
1
2
6
5
HMS
2
6
5
7
3
4
3
4
1
2
6
5
HMT
2
6
5
7
3
4
3
4
1
2
6
5
HMX
2
6
5
7
3
4
3
4
1
2
6
5
HDG
2
6
5
7
3
4
3
4
1
2
6
5
CUSP1
2
6
5
7
3
4
3
4
1
2
6
5
CUSP2
2
6
5
7
3
4
3
4
1
2
6
5
HMT / HDG
2
6
5
7
3
4
3
4
1
2
6
5
Graduality
2
6
5
7
3
4
3
4
1
2
6
5
-10
-15 -5
0
log10(p-value) (Wilcox)
AB
Genetic distance bins Genetic distance bins
1 2 3 4 5 6 7 1 2 3 4 5 6 7
1 2 3 4 5 6 7 1 2 3 4 5 6 7
1 2 3 4 5 6 7 1 2 3 4 5 6 7
1 2 3 4 5 6 7 1 2 3 4 5 6 7
Figure 15. Features show different ranges of strong and weak correlations with genetic distance. A) Binned genetic
distances between all species pairs, as in FIG.14. Corresponding differences in tooth complexity and heterodonty measures,
respectively, are shown on the y-axis. For most measures excluding tooth-level complexities, no important differences are seen
across most of the genetic distance range, suggesting limited depth of phylogenetic signal. The dark line connects averages per
bin, with the grey shadow highlighting the range of the central 50% of all pairs per bin. B) Dissimilarity of heterodonty and
tooth complexity values between bins is quantified by Wilcoxon test, with numbers on x- and y-axes corresponding to bins.
p-values are represented by greyscale; note that they can refer to both increasing and decreasing trends.
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Body size [cm]
-log10(depth) [m]
A
B
-log10(avg.depth)
size.hatch/avg.size
Figure 16. Depth and size ranges per species. This graph displays key traits linked to ecology across the scrutinized taxa. A)
Depth: Thick bars span typical depth range per species as gathered across literature. thin candlestick ends expand to
exceptional reports, red dots and the color code mark averages. B) Body size (length). Range of body sizes for sexually mature
adults are shown. Averages of lower and upper ranges for both sexes are shown as red dots. Colors mark ratios between
hatchling/newborn and average mature adult body length.
Abouheif's C_mean
Pagel's λ
Blomberg's K
Moran's I
Troph
Depth
D_high
D_low
Size
S_hatch
Cx_four
Cx_exec
Cx_ang
HDG
HMS
HMT
HMX
Cusp1
Cusp2
phylogenetic signal
shelf
ocean
deep
bentos
nectos
A
vert
verm
ceph
crust
moll
shore
reef
log10(P(phylogenetic_signal))
B
Abouheif's C_mean
Pagel's λ
Blomberg's K
Moran's I
Troph
Depth
D_high
D_low
Size
S_hatch
Cx_four
Cx_exec
Cx_ang
HDG
HMS
HMT
HMX
Cusp1
Cusp2
shelf
ocean
deep
bentos
nectos
vert
verm
ceph
crust
moll
shore
reef
Figure 17. Phylogenetic signal. Using the commonly used Moran’s I, Abouheif’s C_mean, Pagel’s
λ
and Blomberg’s K, A)
phylogenetic signal strengths and B) the p-values of their respective significances are plotted for ecological traits, tooth
complexity, and heterodonty measures. Most trophic guilds, monognathic heterodonty measures, angle-based complexity
measures, and cuspidities, only show relatively low phylogenetic signal, while some habitat traits, depth, Fourier complexity,
and maximal heterodonty, exhibit strong phylogenetic signal.
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depth_up
ceph
deep
vert
benthos
shelf
ocean
verm
depth
depth_low
shore
reef
troph
moll
size
crust
nectos
depth_up
ceph
deep
vert
benthos
shelf
ocean
verm
depth
depth_low
shore
reef
troph
moll
size
crust
nectos
1
2
1
2
distance
Figure 18. Similarity of canonical correlation profiles between ecological traits. Heatmap showing pair-wise Manhattan
distances between binarized (1/-1) canonical correlation profiles for ecological traits (cf.FIG.6) with tooth complexity measures
(CX1, CX2, CX3, Cusp1, Cusp2) and heterodonties (HMS, HMT, HMTX, HDG) as phenotypic coefficients. Two discernible
sub-clusters emerge, pointing to discrete sets of ecological strategies that are reflected by dental patterns. Red: food guildes,
turquoise: habitat specifiers.
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0.5
0.0
0.3
0.4
0.2
0.1
0.5
0.0
0.2
0.4
0.3
0.1
Cusp1
Cusp2 HMS HMT HDG
| Pearson's R |
| Correlation (CCA) |
5
0
3
4
2
1
- log10 ( p-value lin.regression )
5
0
3
4
2
1
- log10 ( Wilcox P (CCA) )
Figure 19. Summary: Correlations of features per resolution scale and macro-phylogeny do not reflect ecological
importance. For the measures separated by resolution as in FIG.8, we show the respective correlations with macro-phylogeny
(superorders) using Pearson’s correlation R, besides the corresponding p-values, CCA correlation strengths, cf. FIG.6, as well
as the p-values of a Wilcoxon test, cf. FIG.5. The significance threshold of 0.05 for p-values is indicated by a dashed line.
Particularly strong correlations exist for fine-grained complexity (Cusp2) as well as dignathic heterodonty (HDG), which is in
contrast to the scales at which correlations with ecological proxies are most prominent, suggesting relative independence of
ecological and macro-phylogenetic patterns.
HMS
HMT
HMS
HMT
HMT
HMT
HMS
HMT
HMS
HMT
HMT
HMS
HMS
Cusp1
Cusp2 HSQ HTO HDG
CCA Correlations
0.2
0.6
1.0
0.4
1.2
0.8
Figure 20. Scale-dependent ecological relevance is robust against moderate data modifications. A) Upon systematic
removal of shark species from the data set, we recalculated CCA correlations per scale-specific complexity feature (fine, coarse
cuspidity, heterodonties). For each modified data set, we plotted the three inner quartiles and connected the scales by vectors.
Colors indicate which species were removed. B) For the same data, we remove species one-by-one (upper panel) and
ecological predictors (lower panel) and plot p-values for pair-wise t-tests between scale-specific complexity measures. The
color code is logarithmic and significant values (p<0.05) are highlighted in shades of reddish, with the pairs of measures
indicated besides. The results displayed in FIG.8are re-plotted as reference. It appears that the stark differences between
monognathic heterodonty and the other measures shown in FIG.8are robust against moderate alterations to the data.
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Acknowledgements
The authors are grateful to Fumio Nakagawa for his role in building the j-elasmo database as well as for helpful interactions, and
to Arthur Gairin-Calvo, Fidji Berio, Miguel Brun-Usan and Samuel Ginot for critical and constructive comments. This work was
supported by a French ANR grant (ANR-21-CE02-0015 PLASTICiTEETH to N.G.) and a Deutsche Forschungsgesellschaft
DFG research fellowship (ZI1809/1-1:1, Proj.432922638 to R.Z.).
Author contributions statement
R.Z. and N.G. designed project; R.Z. collected data and processed images; R.Z. conducted morphometrics and statistical
analyses; V.T.-S. performed phylogenetic analysis; R.Z. and V.T.-S. made the figures; R.Z. and N.G. wrote the paper. All
authors reviewed the manuscript.
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Additional information
Source of shark dentitions (external: j-elasmo): http://naka.na.coocan.jp/
Ecological data and references: Sharks_eco_refs.xlsx
Genetic sequences used for the phylogeny: List_NCBI_refs.xlsx
Further information will be made available upon request from the corresponding author.
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The copyright holder for this preprintthis version posted November 2, 2024. ; https://doi.org/10.1101/2024.10.30.621069doi: bioRxiv preprint
