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Quantifying shell outline variability in extant and fossil Laqueus (Brachiopoda: Terebratulida): are outlines good proxies for long-looped brachidial morphology and can they help us characterize species?

Published online by Cambridge University Press:  29 December 2020

Natalia López Carranza
Affiliation:
Department of Earth and Planetary Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, U.S.A. E-mail: nlopezc@ku.edu, sjcarlson@ucdavis.edu.
Sandra J. Carlson
Affiliation:
Department of Earth and Planetary Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, U.S.A. E-mail: nlopezc@ku.edu, sjcarlson@ucdavis.edu.

Abstract

Extant and extinct terebratulide brachiopod species have been defined primarily on the basis of morphology. What is the fidelity of morphological species to biological species? And how can we test this fidelity with fossils? Taxonomically and phylogenetically, the most informative internal feature in the brachiopod suborder Terebratellidina is the geometrically complex long-looped brachidium, which is highly fragile and only rarely preserved in the fossil record. Given this, it is essential to test other sources of morphological data, such as valve outline shape, when trying to recognize and identify species. We analyzed valve outlines and brachidia in the genus Laqueus to explore the utility of shell shape in discriminating extant and fossil species. Using geometric morphometric methods, we quantified valve outline variability using elliptical Fourier methods and tested whether long-looped brachidial morphology correlates with shell outline shape. We then built classification models based on machine learning algorithms using outlines as shape variables to predict fossil species’ identities. Our results demonstrate that valve outline shape is significantly correlated with long-looped brachidial shape and that even relatively simple outlines are sufficiently morphologically distinct to enable extant Laqueus species to be identified, validating current taxonomic assignments. These are encouraging results for the study and delimitation of fossil terebratulide species, and their recognition as biological species. In addition, machine learning algorithms can be successfully applied to help solve species recognition and delimitation problems in paleontology, especially when morphology can be characterized quantitatively and analyzed statistically.

Information

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Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of The Paleontological Society
Figure 0

Figure 1. External morphology of Laqueus (Laqueus erythraeus CAS IZ 202358A). A, Dorsal view; B, ventral view; C, lateral view.

Figure 1

Figure 2. Datasets analyzed in this study. A, Dataset 1 consists of 40 computed tomography (CT)–imaged adult individuals classified in three extant species from the NW Pacific (Laqueus blanfordi [n = 1], Laqueus quadratus [n = 2], and Laqueus rubellus [n = 9]) and two from the NE Pacific (Laqueus erythraeus [n = 16] and Laqueus vancouveriensis [n = 12]) (Table 1). All specimens were CT imaged at the Center for Molecular and Genomic Imaging (CMGI) at the University of California, Davis. B, Dataset 2 consists of 99 photographed individuals of two extant species from the NE Pacific (L. erythraeus [n = 50] and L. vancouveriensis [n = 49]) and 16 fossil individuals from the Pliocene of Southern California (Table 2). Extant individuals were photographed by N.L.C. at the University of California, Davis. Fossil individuals were photographed by staff from the Natural History Museum of Los Angeles County Invertebrate Paleontology Collection. All outlines depicted in this figure are size-standardized.

Figure 2

Table 1. Dataset 1. List of computed tomography–imaged specimens analyzed including number of individuals, localities (NW and NE Pacific), and institutional collections from which specimens were obtained (NMNH, National Museum of Natural History, Smithsonian Institution; CAS, California Academy of Sciences; DAV:SJCLab, Carlson, personal collections, University of California, Davis). Small sample sizes reflect the logistical difficulties of collecting many species of extant brachiopods, not only Laqueus, and the collecting limits set by numerous institutions, given diminishing population sizes.

Figure 3

Table 2. Dataset 2. List of photographed specimens analyzed including number of individuals, localities (NE Pacific), and institutional collections from which specimens were obtained (DAV:SJCLab, Carlson, personal collections, University of California, Davis; NMNH, National Museum of Natural History, Smithsonian Institution; LACMIP, Natural History Museum of Los Angeles County, Invertebrate Paleontology Collection).

Figure 4

Figure 3. Outline digitization. A, For dataset 1, image stacks obtained from computed tomography (CT) were sliced parallel to the commissure to visualize the outlines of the dorsal valves of brachiopods. B,C, For dataset 2, individuals were photographed. In the case of extant specimens (B), dorsal valves were photographed; for fossil individuals, depending on the preservation of the specimens, dorsal, ventral, or complete shells were photographed (C). D, Images generated from A to C were manually traced using Adobe Illustrator to create black masks of the contour of the shells, which were then scaled and aligned. E, To digitize the outline coordinates, the black masks were input into the R package Momocs (Bonhomme et al. 2014). Twelve landmarks were placed along the outlines to serve as the starting point for the Procrustes superimposition analyses. Specimens depicted: A, dorsal valve of Laqueus erythraeus CAS IZ 202358F; B, D, and E, dorsal valve of L. erythraeus DAV:SJCLab C5; C, ventral valve with inferred dorsal valve outline (dashed line) of Laqueus vancouveriensis LACMIP 308.30, LACMIP Type 14877.

Figure 5

Figure 4. Landmark and semilandmark scheme for long-looped brachidia. Schematic depiction of a bilateral long-looped brachidium showing the placement of the 34 landmarks (numbered in A and B) and six curves (roman numerals in B) used for the integration test between this structure and shell outlines.

Figure 6

Figure 5. Principal component analysis (PCA) of dataset 1 on valve outlines described by elliptical Fourier coefficients and changes in outline shape corresponding to PCs. A, PC 1–PC 2; B, PC 1–PC 3. Thin-plate spline deformation vector fields showing shape change associated with C, PC 1 (PC 1min: Laqueus erythraeus DAV:SJCLab 0007; PC 1max: Laqueus rubellus USNM PAL 716075); D, PC 2 (PC 2min: Laqueus vancouveriensis USNM PAL 716058; PC 2max: Laqueus quadratus USNM PAL 716076); and E, PC 3 (PC 3min: L. vancouveriensis USNM PAL 716065; PC 3max: L. vancouveriensis USNM PAL 716058). Solid lines represent outlines corresponding to minimum PC values and dashed lines outlines corresponding to maximum PC values. Arrows represent the direction of shape change from minimum to maximum PC values.

Figure 7

Figure 6. Mean shapes of A, Laqueus erythraeus; B, Laqueus quadratus; C, Laqueus rubellus; and D, Laqueus vancouveriensis. E, Outline of Laqueus blanfordi, represented by only one specimen.

Figure 8

Figure 7. Canonical variate analyses (CVAs) on elliptical Fourier coefficients of dataset 1. A, CVA of Laqueus erythraeus, Laqueus quadratus, Laqueus rubellus, and Laqueus vancouveriensis. B, CVA excluding L. quadratus. C, Classification table based on a leave-one-out cross-validation (see text).

Figure 9

Figure 8. Plot of partial least-squares analysis PLS 1 scores for long-looped brachidial shape versus scores for outline shape. To the right, the maximal and minimal extrema of 3D landmark and semilandmark configurations for long loops (x-axis) and thin-plate spline of outlines (y-axis) are depicted. These extrema do not depict actual specimens, but rather show the most extreme shapes inferred from this model for each axis. The horizontal PLS 1 axis describes 3D landmark and semilandmark configurations, which vary from negative to positive extrema as shown above. The vertical PLS1 axis describes outlines, which vary from negative to positive extrema as shown above. As can be seen here, outline shape variation is associated with loop shape variation and vice versa.

Figure 10

Figure 9. Principal component analysis (PCA) of extant individuals of Laqueus erythraeus and Laqueus vancouveriensis, color-coded by species. A, PC 1–PC 2, and B, PC 1–PC 3 of shell shape. C, PC 1–PC 2, and D, PC 1–PC 3 of shell shape and size.

Figure 11

Figure 10. Thin-plate spline deformation vector fields showing changes in outline shape associated with PC axes shown in Fig. 9A,B (size, rotation, and translation normalized). A, PC 1 (PC 1min: DAV:SJCLab C31; PC 1max: USNM PAL 770910); B, PC 2 (PC 2min: USNM PAL 770907; PC 2max: DAV:SJCLab C31); and C, PC 3 (PC 3min: USNM PAL 770881; PC 3max: USNM PAL 770892). Solid lines represent minimum PC values; dashed lines represent outlines associated with maximum PC values.

Figure 12

Figure 11. Principal component analysis (PCA) of extant Laqueus erythraeus and Laqueus vancouveriensis, color-coded by centroid size. A, PC 1–PC 2 of shell shape; B, PC 1–PC 2 of shell shape and size.

Figure 13

Figure 12. Principal component analysis (PCA) of extant Laqueus erythraeus and Laqueus vancouveriensis, color-coded by geographic locality. A, PC 1–PC 2 of outline shape. B, PC 1–PC 2 of outline shape and size. C, Locality map. Because size was not normalized in B, larger individuals plot toward negative PC 1 values, and smaller individuals plot toward positive PC 1 values (see Fig. 11 for color-coded plots according to size).

Figure 14

Figure 13. Density plots of the first linear discriminant (LD 1). A, Density plot, and B, classification table based on a leave-one-out cross-validation for fully normalized outline data (only shape considered). C, Density plot, and D, classification table based on a leave-one-out cross-validation for partially normalized outline data (shape and size considered).

Figure 15

Figure 14. Predicted classification for fossil Laqueus specimens based on models built using random forests algorithm. A, Predicted classification when only shape is considered. B, Predicted classification when both shape and size are considered.