Abstract
The phylogenetic ecology and wing ecomorphology of the Afro-Asian dragonfly genus Trithemis have been investigated previously. Curiously, results reported for the forewing and hindwing shape variation in the latter were, in some ways, at odds with expectations given the mapping of landscape and water-body preferences over the Trithemis cladogram. To confirm these results we conducted a wing-shape investigation of 27 Trithemis species that employed a robust statistical test for phylogenetic covariation, more comprehensive representation of Trithemis wing morphology and a wider range of morphometric data-analysis procedures. Contrary to results published previously, statistical comparisons of forewing and hindwing mean shapes with the Trithemis cladogram revealed no statistically significant pattern of phylogenetic covariation. Moreover, landmark-based and image-based geometric morphometric analysis results, as well as embedded image-contrast deep learning analysis results, all demonstrated that both wings exhibit substantial convergent wing-shape similarities among, and differences between, species that inhabit open and forested landscapes and species that hunt over temporary/standing or running water bodies. Geometric morphometric data and data-analysis methods yielded the worst performance in identifying wing shape distinctions between Trithemis habitat guilds and the direct analysis of wing images using an embedded, image-contrast, convolution (deep learning) neural network delivered the best performance. Bootstrap and jackknife tests confirmed that our results are not artifacts of overtrained discriminant systems or the “curse of dimensionality”. In addition to our conclusions pertaining to Trithemis ecomorphology, the discrepancy between the previous investigation’s results and ours appears to reflect decisions made with regard to the manner in which complex morphological structures are sampled and analyzed. Naturally, results and interpretations of patterns in morphometric data pertain only to the data collected, not necessarily to other aspects of the structures from which those data were collected. For samples of morphologically similar taxa, landmark-based sampling strategies may be effective provided a sufficient number of landmark points distributed across all structures of potential interest exist. However, in a large number of instances analysis of full digital images of the structures under consideration may prove to be a more robust and effective sampling strategy, especially when coupled with analysis via machine learning procedures.
A Hybrid Geometric Morphometric Deep Learning Approach for Cut and Trampling Mark Classification
