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

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 analyzing 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 128x128x128 can be computed in less than 0.1 seconds. 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..., , , # 3DKMI: A MATLAB package to generate shape signatures from Krawtchouk moments and an application to species delimitation in planktonic foraminifera

## Description of the data and file structure

We compiled a dataset of 250 individuals over five species from Ocean Drilling Program Site 925, located on the Ceara Rise in the Equatorial Atlantic, and Integrated Ocean Drilling Program Site U1313 within the North Atlantic gyre. The five species are *Globigerinoidesella fistulosa*, *Menardella exilis*, *Truncorotalia crassaformis*, *Sphaeroidinella dehiscens*, and *Trilobatus sacculifer*. For each individual, approximately 300 2D binary segmented image slices were obtained by CT scanning. As our emphasis was solely on morphology, we obtained binary volume representations of each specimen from these slices by applying some pre-processing steps such as cropping and segmentation. Global threshold values for segmentation are manually determined to remove background noise while preserving shape ...