Ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered
Creators: name: MacLeod, Norman
name: Price, Banjamin
name: Stevens, Zachary
Publisher: name: Zenodo
Year: 2021

description: In the analysis and interpretation of organismal morphology it is important to take a number of factors into consideration. The phylogenetic ecology of the Afro-Asian dragonfly genus <i>Trithemis</i> has been investigated by Damm et al. [1] and <i>Trithemis</i> wing ecomorphology by Outomuro et al. [2]. However, the latter study focused exclusively on a somewhat coarse sampling of forewing and hindwing outlines and reported results that were at odds in some ways with expectations given the mapping of landscape and water-body preference over the Damm et al. [1] <i>Trithemis</i> cladogram. Our investigation of wing shape data from 27 <i>Trithemis</i> species employed a more robust statistical test for phylogenetic covariation, more comprehensive representations of <i>Trithemis</i> wing morphology and a wider range of morphometric data-analysis procedures than the Outomuro et al. [2] study. Results indicate that no statistically significant pattern of phylogenetic covariation exists in our <i>Trithemis</i> forewing and hindwing data, but that both wings exhibit substantial shape differences between species that inhabit open and forested landscapes and between species that hunt over temporary/standing or running water bodies. Among the morphometric analyses performed, geometric morphometric data and data-analysis methods yielded the worst performance in identifying wing shape distinctions between <i>Trithemis</i> 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 of group separations and discriminant-function stability confirm that our results are not artifacts of overtrained discriminant systems or the "curse of dimensionality" despite the modest size of our sample.
description: The primary dataset consists of the original images and process images, the latter of which were used for the data-collection and data-analysis portions of the investigation. However, results for all analytic phases of the investigation, along with software code listings, are also part of the archive.
description: All forewing and hindwing images were segmented from dorsal view, whole-specimen images, mounted in a variably sized image frame against a flat white background, converted from 8-bit RGB color to 8-bit greyscale format and adjusted for consistent average brightness and contrast values. In all cases the two pairs of wings present on each individual were inspected and the best preserved/imaged forewing and hindwing set selected to represent the specimen. In those cases where the best preserved/imaged wing was collected from the body's right side the wing image was mirrored to the left-side orientation to render the wing dataset comparable in pose across all species. Once these processing and pose-standardization procedures had been carried out the processed wing images were written out to separate image files in the non-compressed TIFF file format to form an archive of <i>Trithemis</i> forewing and hindwing images. Plates 1 and 2 were assembled from these archive images. In order to compare our <i>Trithemis</i> ecomorphological wing shape results to those of Outomuro et al. [2] a GM-style morphometric analysis was carried out on a combined landmark-semilandmark dataset that included a set of internal vein-node landmarks as well as peripheral outline landmarks and semilandmarks. In order to compare the GM-style analysis of wing morphology as represented by a sparse set of landmarks and semilandmarks with a mathematically equivalent direct analysis of wing images, subsets of these same forewing (<i>n</i> = 217) and hindwing (<i>n</i> = 227) images that did not include labels in the image frame were processed to standardize their frame sizes, image sizes, orientations, and pixel color scales in order to render their images geometrically comparable. In order to determine whether morphological distinctions between habitat categories could be improved and/or clarified by adopting a non-linear style of discriminant analysis, a "deep learning" convolution neural network (CNN) was employed to analyze the image datasets directly.