Geometric morphometrics out-perform linear-based methods in the
taxonomic resolution of a mammalian species complex
Abstract
Morphology-based taxonomic research frequently applies linear
morphometrics (LMM) in skulls to quantify species distinctions. The
choice of which measurements to collect generally relies on the
expertise of the investigators or a set of standard measurements, but
this practice may ignore less obvious or common discriminatory
characters. In addition, taxonomic analyses often ignore the potential
for subgroups of an otherwise cohesive population to differ in shape
purely due to size differences (or allometry). Geometric morphometrics
(GMM) is more complicated as an acquisition technique, but can offer a
more holistic characterization of shape and provides a rigorous toolkit
for accounting for allometry. In this study, we used linear discriminant
analysis to assess the discriminatory performance of four published LMM
protocols and a 3D GMM dataset for three clades of antechinus known to
differ subtly in shape. We assessed discrimination of raw data (which
are frequently used by taxonomists); data with isometry removed; and
data after allometric correction. We found that group discrimination
among raw data was high for LMM, possibly inflated relative to GMM when
visualised in PCA plots. However, GMM produced better results in group
discrimination after the size and allometry treatments. High measurement
redundancy in LMM protocols appears to result in relatively high
allometry but low discriminatory performance. These findings suggest
that taxonomic measurement protocols might benefit from GMM-based pilot
studies, because this offers the option of differentiating allometric
and non-allometric shape differences between species, which can then
inform on the development of the easier-to-apply LMM protocols.