Tooth shape acquisition
We dissected 63 species that cover both the phylogenetic and dietary
diversity of Alethinophidians (Supplementary Material 1 & 2). Our
sample is composed of adult or sub-adult specimens from museums (AMNH,
MNHN, Jerusalem University) and private collections (details in
Supplementary Material 1 & 2). While comparisons of the shape of the
teeth from different bones show a significant ecological signal in some
clades (Fischer et al., 2022) we here decided to focus only on the
dentary bone to ensure functional homology. In snakes, the dentary is
involved in capturing, restraining, and manipulating the prey; so, it
endures loads related to both prey characteristics (e.g., hardness) and
feeding behavior (e.g., holding a vigorous prey, extracting a snail from
its shell). The maxilla has a similar function, but it is too
functionally and morphologically derived in venomous snakes to be able
to make a fair comparison. We isolated the right dentary bone of the
specimens and CT scanned it using a Phoenix Nanotom S μCT‐scanner
(General Electric, Fairfield, CT, USA) at the Institut de Genomique
Fonctionelle, Ecole Normale Superieure (Lyon, France). ForCrotalus adamanteus , Eryx jaculus and Dasypeltis
scabra , we scanned the left dentary and mirrored the mesh. The bones
were scanned at mid-section to ensure homology in position and as close
as possible to the teeth to get the highest possible resolution (voxel
size between 0.97-7.50μm), with a voltage of 100kV and a current of
70μA. Only the Python regius tooth was not a mid-section but the
first tooth of the dentary (but removing it from the sample barely
changed the statistical results). The 3D reconstruction was done using
Phoenix datos|x2 (v2.3.0, General Electric, Fairfield, CT, USA)
and the subsequent segmentation was performed using VGStudioMax (v1.0,
Volume Graphics GmbH, Heidelberg, Germany). We segmented teeth that were
not broken or in the process of being replaced, which was easily
noticeable on the scans through resorption of the bony attachment in
most species. We obtained one tooth per specimen, so 63 dentary teeth.
Comparisons between single teeth (from different bones) in
phylogenetically diverse samples have demonstrated an ecological signal
(Fischer et al., 2022), and the interspecific shape variation is larger
than intraspecific variation in our sample, validating the use of one
tooth per species (see Supplementary Material 2b).
We used 3D geometric morphometrics to quantify the 3D shape of the
teeth. We placed 14 anatomical landmarks: 7 on the outer surface of the
teeth, 7 on the pulp cavity surface (inner part of the tooth
Supplementary Material 3) and 100 curve semi-landmarks (50 on each
layer) using the software MorphoDig 1.2 (Lebrun, 2017). Curves
correspond to the anterior and posterior edge of both layers and to the
limit of the tooth insertion onto the bone. In addition, 42 and 65
surface semi-landmarks were respectively placed on the inner and outer
surfaces of the teeth to obtain an accurate 3D representation of the
tooth shape (Fig. 1). This template allows us to obtain information both
on their shape and on the thickness of the hard tissue material (dentine
and enamel). We placed the anatomical landmarks and curve semi-landmarks
by hand on each specimen and checked for the repeatability of the
positioning by digitizing these six times in five specimens for which
the teeth looked similar. A Principal Component Analysis demonstrated
the repeatability of the positioning of our 14 anatomical landmarks and
the necessity of using curve and surface semi-landmarks (Supplementary
Material 4). We used the ‘Morpho’ package (Schlager, 2015) to project
and relax the surface semi-landmarks on each specimen and to slide the
curve and surface semi-landmarks while minimizing the bending energy
between the specimen and the template (Gunz & Mitteroecker, 2013). We
then performed a Procrustes superimposition using the functiongpagen of the ‘geomorph’ package (Adams et al., 2020), the
resulting Procrustes coordinates were used to test our hypotheses. The
projection, relaxation and Procrustes superimposition were performed
using R version 3.4.4 (R Core Team, 2018).
As 3D geometric morphometrics removes size information, we also took
linear and angle measurements of the teeth to test their potential link
with dietary ecology. We measured the length of the curvature
(LC) along with maximal and mean curvature
(κmean, κmax) of each teeth using the
FIJI (v1.53q) plugin ‘Kappa’ (Mary & Brouhard, 2019). To do so, we used
a snapshot of the medial side of each tooth, obtained in GeomagicStudio
2013 (3D Systems, Rock Hill). We digitized the midline to obtain its
global curvature (see examples in Supplementary Material 5, raw
measurements available in Supplementary Material 1). From the mean and
max curvature measurements, we calculated the corresponding degree of
curvature (DCmean, DCmax) using the
following equation:
\(D_{C}=\left(\ \kappa\ \times L_{C}\ \right)\ mod\ 2\pi\ \times\ \frac{180}{\pi}\)