Skip to main content
Dryad logo

What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered

Citation

MacLeod, Norman; Price, Banjamin; Stevens, Zachary (2022), What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered, Dryad, Dataset, https://doi.org/10.5061/dryad.41ns1rnf3

Abstract

Abstract Background The phylogenetic ecology of the Afro-Asian dragonfly genus Trithemis has been investigated previously by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010) and wing ecomorphology by Outomuro et al. (in J Evol Biol 26:1866–1874, 2013). However, the latter investigation employed 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 Trithemis cladogram produced by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010). To further explore the link between species-specific wing shape variation and habitat we studied a new sample of 27 Trithemis species employing a more robust statistical test for phylogenetic covariation, more comprehensive representations of Trithemis wing morphology and a wider range of morphometric data-analysis procedures. Results Contrary to the Outomuro et al. (in J Evol Biol 26:1866–1874, 2013) report, our results indicate that no statistically significant pattern of phylogenetic covariation exists in our Trithemis forewing and hindwing data and that both male and female wing datasets exhibit substantial shape differences between species that inhabit open and forested landscapes and species that hunt over temporary/standing or running water bodies. Among the morphometric analyses performed, landmark data and geometric morphometric data-analysis methods yielded the worst performance in identifying ecomorphometric shape distinctions between Trithemis habitat guilds. Direct analysis of wing images using an embedded 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. Conclusion Our results suggest that Trithemis wing morphology reflects the environment’s “push” to a much greater extent than phylogeny’s “pull”. In addition, they indicate that close attention should be paid to the manner in which morphologies are sampled for morphometric analysis and, if no prior information is available to guide sampling strategy, the sample that most comprehensively represents the morphologies of interest should be obtained. In many cases this will be digital images (2D) or scans (3D) of the entire morphology or morphological feature rather than sparse sets of landmark/semilandmark point locations.

Methods

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 Trithemis forewing and hindwing images. Plates 1 and 2 were assembled from these archive images.

In order to compare our Trithemis ecomorphological wing shape results to those of Outomuro et al. 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 (n = 217) and hindwing (n = 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.

Usage Notes

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.

Funding