Morphometric data.
We examined 335 adult specimens (fully erupted M3;
following age classes from
Arellano et al. 2012) deposited in
mammal collections and classified at the time of collection as R.
mexicanus . However, due to the cryptic diversity reported for this
species, only individuals that, 1) had a known genetic identity or 2)
were collected within less than 60 km of an individual with a known
genetic identity were used in the morphometric analyses. In total, 69
specimens (31 males, 38 females) were selected for morphometric analysis
(Fig. 1; Appendix 1) and grouped according to the species proposed by
the molecular delimitation methods. Individuals from El Salvador and
Colombia (R. mexicanus clade IIB and clade IIIA in Fig. 2) were
excluded due to the low sample size (n ≤ 5). Digital images of the
dorsal and ventral view of the skull of each specimen were taken using
an Olympus DP73 Digital Camera and a millimeter rule as a scale bar.
For both views, we digitized
landmark and semilandmark configurations as in
Martínez-Borrego et al. (2022b)
using TPSdig 2.31 (Rohlf 2015).
Configurations were alignment, rotate, and scaled under a Generalized
Procrustes Analysis (Rohlf and
Slice 1990) in the R package geomorph 4.04.0
(Adams et al. 2022). In the case
of semilandmarks, they were aligned by sliding points along their
tangent vectors until reaching the point of minimum bending energy
(Bookstein 1997; Zelditch et al.
2004). Configurations superimposition outputs were the shape variables
(Procrustes Distances and Procrustes Coordinates) and the centroid size
(CS).
Morphometric comparison
between delimited species.
We first explored the shape variation in both views of the skull using a
Principal Component Analysis (PCA) of the Procrustes Coordinates to
visualize clustering and reduce the dimensionality of the data. We
tested skull shape differences related to sex and delimitated species
using a Procrustes ANOVA model (Klingenberg and McIntyre 1998), where we
used the Procrustes distance variance to estimate shape variance in each
factor. The factorial design included shape as the dependent variable,
sex and species as the main factor, and CS as a covariate to also
evaluate the effect of the skull size on the shape variation
(allometry). Additionally, we used a residual resampling procedure, on
the reduced model (Shape~CS), based on 1000 iterations,
to assign the significances of the F statics of the model. These
analyses were performed in the package geomorph. We also performed
pairwise comparison tests to quantify differences in skull shape between
delimited species in the R package MORPHO 2.4 (Schlager 2016) employing
the Procrustes Distances, 1000 permutations, and a significance level ofp ≤ 0.05. Differences in the CS between sexes and delimitated
species were assessed in the R package RRPP (Collyer and Adams 2018)
using an OLS estimation method and the same resampling procedure
described above.
To evaluate cranial shape differences between putative species included
in the morphometric analyses we performed a canonical variates analysis
(CVA). Because CVA cannot be performed when the number of variables is
greater than the sample size per group (Kovarovic et al. 2011), we only
used the shape information from the first 10 principal components (90%
and 92% of the variance explained for dorsal and ventral views,
respectively). Finally, we performed Linear discriminant analysis (LDA)
to assess the discrimination of individuals against the different
delimited species based on skull shape. A cross-validation procedure
from the CVA scores was employed to obtain the correct discrimination
rate, which allowed us to verify the effectiveness of the LDA to
discriminate taxa into their correct groups. For these analyses, we used
the R packages geomorph and MASS 7.3-51.4 (Ripley et al. 2020).