Journalartikel

Jahr der Veröffentlichung: 2010

Seiten: 497-515

Zeitschrift: Paleobiology

Bandnummer: 36

Heftnummer: 3

ISSN: 0094-8373

DOI Link: https://doi.org/10.1666/08068.1

Verlag: Cambridge University Press

Abstract: 
Biometric analyses are useful tools for the study of organisms, their phylogenetic affiliation, and the pattern and rate of their evolution. Various morphometric techniques have been developed to analyze morphological variation, but methodological choices are often made arbitrarily because quantitative comparisons are lacking or inconclusive. Here we address morphometric quantification of taxa with few unambiguously identifiable landmarks (<15), utilizing ornamented and unornamented gastropod shells. Support vector machines were applied to evaluate classification performances of landmark (LMA), elliptic Fourier (EFA), and semi-landmark analysis (SLM). This evaluation is based on the discrimination of between-group differences relative to within-group variation, and thus allows comparing how the techniques treat different types of biological information. The results suggest that EFA performs slightly better than SLM (and certainly LMA) in discerning a priori identified taxa with unornamented shells, but that SLM is significantly superior to other techniques for ornamented shells. Alignment and homology problems may cause the subtle variations in ornamentation to become blurred as noise in EFA, even though EFA is often cited to be able to deal with complex shapes. Performance of LMA depends entirely on how accurately the structure can be covered with landmarks. Guidelines in choosing a morphometric technique in diverse cases are provided.

Harvard-Zitierstil: Van Bocxlaer, B. and Schultheiß, R. (2010) Comparison of morphometric techniques for shapes with few homologous landmarks based on machine-learning approaches to biological discrimination, Paleobiology, 36(3), pp. 497-515. https://doi.org/10.1666/08068.1

APA-Zitierstil: Van Bocxlaer, B., & Schultheiß, R. (2010). Comparison of morphometric techniques for shapes with few homologous landmarks based on machine-learning approaches to biological discrimination. Paleobiology. 36(3), 497-515. https://doi.org/10.1666/08068.1