10.5061/DRYAD.MM5M1
Hetherington, Alexander J.
University of Bristol
Sherratt, Emma
University of Manchester
Ruta, Marcello
University of Lincoln
Wilkinson, Mark
University of Bristol
Deline, Bradley
University of West Georgia
Donoghue, Philip C. J.
University of Bristol
Data from: Do cladistic and morphometric data capture common patterns of
morphological disparity?
Dryad
dataset
2016
disparity
constraint
Caecilia
morphospace
2016-02-17T00:00:00Z
2016-02-17T00:00:00Z
en
https://doi.org/10.1111/pala.12159
167936 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
The distinctly non-random diversity of organismal form manifests itself in
discrete clusters of taxa that share a common body plan. As a result,
analyses of disparity require a scalable comparative framework. The
difficulties of applying geometric morphometrics to disparity analyses of
groups with vastly divergent body plans are overcome partly by the use of
cladistic characters. Character-based disparity analyses have become
increasingly popular, but it is not clear how they are affected by
character coding strategies or revisions of primary homology statements.
Indeed, whether cladistic and morphometric data capture similar patterns
of morphological variation remains a moot point. To address this issue, we
employ both cladistic and geometric morphometric data in an exploratory
study of disparity focussing on caecilian amphibians. Our results show no
impact on relative intertaxon distances when different coding strategies
for cladistic characters were used or when revised concepts of homology
were considered. In all instances, we found no statistically significant
difference between pairwise Euclidean and Procrustes distances, although
the strength of the correlation among distance matrices varied. This
suggests that cladistic and geometric morphometric data appear to
summarize morphological variation in comparable ways. Our results support
the use of cladistic data for characterizing organismal disparity.
Hetherington_AJ_et_al_ESMCladistic and geometric morphometric datasets on
which this study is based.