The efficiency of classical metric descriptors (MDs), normalised elliptic Fourier coefficients (EFCs), and the power series of normalised elliptic Fourier coefficients (PEFCs) in revealing variation in leaf shape was evaluated and compared for species in the genus Chaenomeles, using canonical variates analyses (CVAs) and reclassification tests. For EFCs 30 harmonics (and for PEFCs 40 harmonics) were needed to achieve 100% correct reassignment of plants. By contrast, MDs were considerably less efficient and only 87%, 77% and 66% of the plants were correctly reassigned to species, populations, and maternal families, respectively. Furthermore, when compared with data based on molecular random amplified polymorphic DNA markers (RAPDs), PEFCs produced the most concordant data sets as revealed by cluster analyses. To obtain comparable estimates of variability and differentiation, the AMOVA approach was used both for molecular data and, as a novelty, for quantitative morphometric data. The variation was partitioned by hierarchical extraction of variance components from matrices of standardised squared Euclidean distances, and differentiation estimates were obtained as Φ-statistics. All descriptor sets partitioned the variance in a similar way. Surprisingly high correlations were found between RAPDs and elliptic Fourier transforms for the among-family estimates of variance components (RAPDs vs. PEFCs: 0.89, P = 0.003; RAPDs vs. EFCs: 0.86, P = 0.006). In contrast, only a moderate correlation was found between RAPDs and MDs (0.71, P = 0.049). This may indicate that shape per se (the size component excluded) is a less biased estimator of genetic variation than metric leaf descriptors in the genus Chaenomeles. Estimates of population differentiation based on EFCs and PEFCs were always lower than differentiation estimates based on RAPDs, whereas the differentiation estimates based on MDs were in general higher than estimates based on RAPDs, except for one species including hybrid populations.
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