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Open Access Short Report

The exchangeability of shape

Jean-Pierre AL Dujardin12*, Dramane Kaba3 and Amy B Henry4

Author Affiliations

1 Department of Medical Entomology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

2 Institute for Research and Development, Montpellier, France

3 Institute Pierre Richet/INSP, BP. V 47, Abidjan, Ivory Cost

4 Asia-Pacific Institute of Tropical Medicine and Infectious Diseases, University of Hawaii at Manoa, Honolulu, United States

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BMC Research Notes 2010, 3:266  doi:10.1186/1756-0500-3-266

Published: 22 October 2010



Landmark based geometric morphometrics (GM) allows the quantitative comparison of organismal shapes. When applied to systematics, it is able to score shape changes which often are undetectable by traditional morphological studies and even by classical morphometric approaches. It has thus become a fast and low cost candidate to identify cryptic species. Due to inherent mathematical properties, shape variables derived from one set of coordinates cannot be compared with shape variables derived from another set. Raw coordinates which produce these shape variables could be used for data exchange, however they contain measurement error. The latter may represent a significant obstacle when the objective is to distinguish very similar species.


We show here that a single user derived dataset produces much less classification error than a multiple one. The question then becomes how to circumvent the lack of exchangeability of shape variables while preserving a single user dataset. A solution to this question could lead to the creation of a relatively fast and inexpensive systematic tool adapted for the recognition of cryptic species.


To preserve both exchangeability of shape and a single user derived dataset, our suggestion is to create a free access bank of reference images from which one can produce raw coordinates and use them for comparison with external specimens. Thus, we propose an alternative geometric descriptive system that separates 2-D data gathering and analyzes.