Material & Methods
For resolving SSD and SShD, we investigated 227 crocodile newt specimens
of the genera Echinotriton and Tylototriton housed in
natural history collections (Supplementary Table S1) including the
following: E. andersoni from Okinawa Island, Tylototriton
asperrimus, T. himalayanus, T. kweichowensis, T. shanjing, T. shanorum,
T. taliangensis, T.uyenoi and T. verrucosus . The selected
species represent all major clades of crocodile newts comprising the
different mating modes i.e., showing a circle dance or applying an
amplexus (Pogoda et al., 2020). Following species are generally thought
to be circle dancers: E. andersoni, T. asperrimus, T.
kweichowensis, T. shanjing , while the others are regularly observed to
apply an amplexus. Tylototriton asperrimus represent the
only member of the subgenus Yaotriton with a sufficient sample
size of both sexes whereas unfortunately not enough female specimens of
other species were available in natural history collections due to a
heavy male biased field sampling during the breeding season. To access
osteology for SD analyses, specimens were µCT-scanned. CT-scans were
carried out either with a Bruker SkyScan1272 with the software NRecon
(Bruker CT) for reconstructions or within the X-ray imaging laboratory
at the Institute for Photon Science and Synchrotron Radiation, Karlsruhe
Institute of Technology (KIT) employing a microfocus x-ray tube
(XWT-225, X-RAY WorX, Garbsen, Germany) and a flat panel detector (XRD
1621 CN14 ES, PerkinElmer, Waltham, USA) in combination with a custom
designed mechanical sample manipulator. For the CT scans made at KIT,
Octopus 8.6 (Inside Matters, Gent, Belgium) was used to perform the
tomographic reconstruction. The scan resolution was either 20.1
(SkyScan) or 21.3 µm (KIT-custom build scanner).
To catch the entire shape variation of the cranium 45 three-dimensional
(3D) landmarks were digitized and for the analysis of the humerus shape
six fixed landmarks and 50 semi-landmarks in three curves were digitized
(Fig.1). Prior to landmark digitization, potential error in setting
landmarks was validated by digitizing one specimen five times and five
additional specimens of the same species to compare consistent placement
by Procrustes distance of the respective mean shapes. Landmark
digitization was carried out by one author with the software Checkpoint
v.2019.03.04.1102 (Stratovan Ltd.). Geometric morphometrics was
performed in R version 3.6.3 (R Development Core Team, 2019) using the
packages geomorph v.3.2.1, RRPP v. 0.5.2 and Morpho 2.8 (Schlager, 2017,
Collyer & Adams, 2018, Adams et al., 2019). Complete landmark
configurations are a prerequisite for GM analyses. Hence, missing
landmarks (due to anomalies or injuries) were first estimated by thin
plate spline approach implemented in the function ‘estimate.missing’
(Gunz et al., 2009). Semi-landmarks in the humerus dataset were equally
spaced along the digitized curve. Variation due to location, rotation
and scale was removed by a generalized Procrustes alignment (GPA) using
the function ‘gpagen’ (Rohlf & Slice, 1990). In the humeri dataset,
semi-landmarks were simultaneously slided using minimized bending energy
(Bookstein, 1997a, Perez et al., 2006). As asymmetry was not in the
scope of this study, bilateral landmarks in cranial landmark
configuration were symmetrized by averaging left and right landmark
pairs. Skulls and humeri were analysed further in the same approach. A
principal component analysis (PCA) on Procrustes coordinates was
performed and plotted to investigate general shape variation. To account
for size, we used logarithm of centroid size (CS), which represents a
measure of size in GM (Bookstein, 1997b, Zelditch et al., 2012).
A full factorial model design including species and mating mode was
precluded by the model system as each species comprises only a single
mating mode. Thus, several Procrustes ANOVAs had to be performed to
investigate all potential sources of morphological variation. First, a
Procrustes ANOVA as implemented in the function ‘procD.lm’ with size,
species and sex including all interactions was performed. Allometry
between sexes was not different, indicated by non-significant
interaction between sex and CS. Thus, we explored allometric shape
change by another Procrustes ANOVA including CS and sex only. To test
whether mating mode affects the pattern of SD, we first ran a model
design including only sex, mating mode and its interaction, and second a
model including these factors plus CS as covariate. To explore different
patterns of SShD, we performed a trajectory analysis to visualize shape
change directions between species and performed a group mean prediction
with 95%-confidence intervals for males and females in each species,
implemented in the function ‘predict.lm.rrpp’ in the RRPP package.
Sexual size dimorphism patterns between species and mating modes were
estimated by a Procrustes ANOVA of species and sex on CS and, in a
second one, sex and mating mode as variables. The function ‘pairwise’
was used to reveal which groups were different. According to the model,
a grouping variable of sex with species or mating mode was used.
Significance testing was performed using Residual Randomization by
10.000 random permutations (Collyer et al., 2015, Collyer & Adams,
2018). Shape changes were visualized as TPS-grids by warping the mean
shape by thin-plate spline approach with the function ‘plotRefToTarget’.