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      3DKMI: A MATLAB package to generate shape signatures from Krawtchouk moments and an application to species delimitation in planktonic foraminifera

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          Abstract

          • The rapid and repeatable characterization of individual morphology has advanced automated taxonomic classification. The most direct study of evolutionary processes is, however, not from taxonomic description, but rather of the evolution of the traits that comprise individuals and define species. Repeatable signatures of individual morphology are crucial for analysing the response to selection at scale, and thus tracking evolutionary trajectories through time and across species boundaries.

          • Here, we introduce our 3DKMI—an open‐source MATLAB package designed for the study of morphology using three‐dimensional (3D) Krawtchouk moment invariants. The volumetric features derived from the 3D images remain stable under translation, scaling and rotation and, for an image of size 128 × 128 × 128 can be computed in less than 0.1 s.

          • We applied our package as a case study on a collection of 300 X‐ray computed tomography scans of planktonic foraminifera specimens across five species to (1) assess the invariance of the features under different transformations and (2) analyse morphological differences among species based on the extracted characteristics.

          • We show that 3DKMI has the capacity to efficiently and repeatedly characterize the signatures of individual morphology. In the future, we hope that the 3D feature extraction technique 3DKMI will be widely applied to digital collections to advance research in ecology and evolution.

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          Sequence-Based Species Delimitation for the DNA Taxonomy of Undescribed Insects

          Cataloging the very large number of undescribed species of insects could be greatly accelerated by automated DNA based approaches, but procedures for large-scale species discovery from sequence data are currently lacking. Here, we use mitochondrial DNA variation to delimit species in a poorly known beetle radiation in the genus Rivacindela from arid Australia. Among 468 individuals sampled from 65 sites and multiple morphologically distinguishable types, sequence variation in three mtDNA genes (cytochrome oxidase subunit 1, cytochrome b, 16S ribosomal RNA) was strongly partitioned between 46 or 47 putative species identified with quantitative methods of species recognition based on fixed unique ("diagnostic") characters. The boundaries between groups were also recognizable from a striking increase in branching rate in clock-constrained calibrated trees. Models of stochastic lineage growth (Yule models) were combined with coalescence theory to develop a new likelihood method that determines the point of transition from species-level (speciation and extinction) to population-level (coalescence) evolutionary processes. Fitting the location of the switches from speciation to coalescent nodes on the ultrametric tree of Rivacindela produced a transition in branching rate occurring at 0.43 Mya, leading to an estimate of 48 putative species (confidence interval for the threshold ranging from 47 to 51 clusters within 2 logL units). Entities delimited in this way exhibited biological properties of traditionally defined species, showing coherence of geographic ranges, broad congruence with morphologically recognized species, and levels of sequence divergence typical for closely related species of insects. The finding of discontinuous evolutionary groupings that are readily apparent in patterns of sequence variation permits largely automated species delineation from DNA surveys of local communities as a scaffold for taxonomy in this poorly known insect group.
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            Visual pattern recognition by moment invariants

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              Image analysis via the general theory of moments*

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                Author and article information

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                Journal
                Methods in Ecology and Evolution
                Methods Ecol Evol
                Wiley
                2041-210X
                2041-210X
                November 2024
                September 26 2024
                November 2024
                : 15
                : 11
                : 1940-1948
                Affiliations
                [1 ] Electronics and Computer Science University of Southampton Southampton UK
                [2 ] Creative Computing Institute University of the Arts London London UK
                [3 ] Life Sciences Natural History Museum London UK
                [4 ] Ocean and Earth Science University of Southampton Waterfront Campus, National Oceanography Centre Southampton Southampton UK
                [5 ] Earth and Planetary Sciences Yale University New Haven Connecticut USA
                Article
                10.1111/2041-210X.14388
                32a6eb52-decb-405b-a6df-476002cda24b
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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