e-journal

Incomplete specimens in geometric morphometric analyses

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1. The analysis ofmorphological diversity frequently relies on the use ofmultivariate 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-data set approach to compare variation in missing data estimation and its effect on geometric
morphometric analyses across taxonomically variable groups, landmark position and sample sizes.
2. Missing morphometric landmark data were simulated from five real, complete data sets, 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 data set. Procrustes superimposition
was used to compare the eigenvectors and principal component scores of a geometric morphometric analysis of
the original landmark data, to data sets 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 with a geometric-morphometric-specific estimator. For most data sets 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 andmay be improved by
a better understanding of shape variation present in a data set. 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 data set 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 data set and landmark independently, as the effectiveness of estimators can vary strongly and unpredictably between different taxa and structures.

Key-words: fossils, geometric morphometrics, missing data estimation, principal component analysis,procrustes analysis,Rstatistical software, shape

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Ketersediaan

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Informasi Detail

Judul Seri

Methods in Ecology and Evolution

No. Panggil

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Penerbit

: British Ecological Society., 2014

Deskripsi Fisik

Methods in Ecology and Evolution 2014, 5, 16–26

Bahasa

English

ISBN/ISSN

doi: 10.1111/2041-21

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

BIOLOGI

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

Lampiran Berkas

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