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Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification

Christodoulou, M. D., Battey, N. H. and Culham, A. ORCID: (2018) Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification. PLoS ONE, 13 (10). e0205357. ISSN 1932-6203

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To link to this item DOI: 10.1371/journal.pone.0205357


Morphological classification of living things has challenged science for several centuries and has led to a wide range of objective morphometric approaches in data gathering and analysis. In this paper we explore those methods using apple cultivars, a model biological system in which discrete groups are pre-defined but in which there is a high level of overall morphological similarity. The effectiveness of morphometric techniques in discovering the groups is evaluated using statistical learning tools. No one technique proved optimal in classification on every occasion, linear morphometric techniques slightly out-performing geometric (72.6% accuracy on test set versus 66.7%). The combined use of these techniques with post-hoc knowledge of their individual successes with particular cultivars achieves a notably higher classification accuracy (77.8%). From this we conclude that even with pre-determined discrete categories, a range of approaches is needed where those categories are intrinsically similar to each other, and we raise the question of whether in studies where potentially continuous natural variation is being categorised the level of match between categories is routinely set too high.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Central Services > Academic and Governance Services > University Museums and Special Collections
ID Code:79658
Uncontrolled Keywords:morphometrics, statistical learning, apples, botany, taxonomy, plant science, Malus, cultivar
Publisher:Public Library of Science


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