Data from: Incomplete specimens in geometric morphometric analyses

1.The analysis of morphological diversity frequently relies on the use of
multivariate methods for characterizing biological shape. However, many of
these methods are intolerant of missing data, which can limit the use of
rare taxa and hinder the study of broad patterns of ecological diversity
and morphological evolution. This study applied a mutli-dataset approach
to compare variation in missing data estimation and its effect on
geometric morphometric analysis across taxonomically-variable groups,
landmark position and sample sizes. 2.Missing morphometric landmark data
was simulated from five real, complete datasets, including modern fish,
primates and extinct theropod dinosaurs. Missing landmarks were then
estimated using several standard approaches and a
geometric-morphometric-specific method. The accuracy of missing data
estimation was determined for each estimation method, landmark position,
and morphological dataset. Procrustes superimposition was used to compare
the eigenvectors and principal component scores of a geometric
morphometric analysis of the original landmark data, to datasets with A)
missing values estimated, or B) simulated incomplete specimens excluded,
for varying levels of specimens incompleteness and sample sizes.
3.Standard estimation techniques were more reliable estimators and had
lower impacts on morphometric analysis compared to a
geometric-morphometric-specific estimator. For most datasets and
estimation techniques, estimating missing data produced a better fit to
the structure of the original data than exclusion of incomplete specimens,
and this was maintained even at considerably reduced sample sizes. The
impact of missing data on geometric morphometric analysis was
disproportionately affected by the most fragmentary specimens. 4.Missing
data estimation was influenced by variability of specific anatomical
features, and may be improved by a better understanding of shape variation
present in a dataset. Our results suggest that the inclusion of incomplete
specimens through the use of effective missing data estimators better
reflects the patterns of shape variation within a dataset than using only
complete specimens, however the effectiveness of missing data estimation
can be maximized by excluding only the most incomplete specimens. It is
advised that missing data estimators be evaluated for each dataset and
landmark independently, as the effectiveness of estimators can vary
strongly and unpredictably between different taxa and structures.