DOI: 10.5061/dryad.41ns1rnf3
title: What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered

description: 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.
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 <em>Trithemis</em> forewing and hindwing images.
 Plates 1 and 2 were assembled from these archive images.
 In order to compare our <em>Trithemis</em>
 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
 (<em>n</em> = 217) and hindwing (<em>n</em> = 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.
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.