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

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.

Morphometric analysis ofSkiagia-plexus acritarchs from the early Cambrian of North Greenland: toward a meaningful evaluation of phenotypic plasticity

“…The taxonomic subdivision of the genus Skiagia was then assessed using linear discriminant analysis (LDA), which maximizes between-group variance (e.g., Albrecht 1980;Campbell and Atchley 1981;Strauss 2010;Huntley 2011). This ordination technique has been widely used in paleontology to help discriminate species based on morphological criteria in groups as varied as sharks (Marramà and Kriwet 2017), killifish (Reichenbacher et al 2007), plants (Álvarez et al 2009), bees (De Meulemeester et al 2012), bryozoans (Cheetham 1986), mollusks (Geary 1992;Marko and Jackson 2001;Reyment 2003), trilobites (Labandeira and Hughes 1994), and brachiopods (Kowalewski et al 1997;Carranza and Carlson 2021). LDA has also been used as a quantitative tool for taxonomic revision of various upper Cambrian and Ordovician acritarch taxa (Wang et al 2017;Yan et al 2017;Kroeck et al 2020).…”

Morphological evolution during the last hurrah of the trilobites: morphometric analysis of the Devonian asteropyginid trilobites

“…To normalize (i.e., eliminate variation due to size, rotation, and translation) the outline coordinates before the elliptical Fourier analysis (EFA), a GPA was performed using three control landmarks: (1) anterior-most point of the glabella in the sagittal line, (2) interior-most point of right S3, and (3) interior-most point of left S3. Once outline coordinates were aligned, an EFA was performed using 99% of harmonic power (Bonhomme et al 2014; López Carranza and Carlson 2021). The resulting morphological variables (EF coefficients) were then used as input for a principal component analysis (PCA).…”

“…The Asteropyginae has not previously been subjected to quantitative morphometric analysis despite broad interest in its distinctive morphologies and the fact that it has been incorporated in several phylogenetic studies, including Lieberman and Kloc (1997), Bignon and Crônier (2013), and Bignon et al (2014), with their broader phylogenetic placement also considered by Edgecombe (1993). Here we utilize both landmark-based geometric morphometric (GM; for applications to trilobites, see Smith and Lieberman 1999; Sheets et al 2004; Crônier et al 2005, 2015; Crônier and Fortey 2006; Hopkins and Webster 2009; Abe and Lieberman 2012; Bignon and Crônier 2012; Hopkins and Pearson 2016; Álvaro et al 2018) and outline-based (Crônier et al 1998, 2005; López Carranza and Carlson 2021)/elliptical Fourier (EF) methods (for applications to trilobites and their close relatives, see Foote 1989; Crônier et al 1998, 2005; Hopkins 2014; Jackson and Budd 2017) in order to consider and also compare and contrast different types of quantitative information on the morphology of cephalic sclerites. Cephala have been frequently used in landmark-based analyses of trilobite morphology (e.g., Hughes 1994; Smith and Lieberman 1999; Adrain 2005; Crônier et al 2005, 2015; Crônier and Fortey 2006; Webster and Zelditch 2005; Webber and Hunda 2007; Hopkins 2011, 2017; Abe and Lieberman 2012; Monti 2018; Webster and Sundberg 2020), because they contain abundant character information often used to define species and higher taxonomic categories, are abundantly preserved, and are considered a valuable repository of information on overall trilobite shape.…”

How we study cryptic species and their biological implications: A case study from marine shelled gastropods

“…; Gould, 2002, 785 ff. ; for example, see, e.g., Morard et al, 2016 [foraminifera]; Budd et al, 1994; Knowlton et al, 1992 [corals]; Chiba, 2007; Herbert & Portell, 2004; Hills et al, 2012; Michaux, 1987, 1989, 1995 [gastropods]; Jackson & Cheetham, 1990, 1994 [bryozoans]; López‐Carranza & Carlson, 2021 [brachiopods]; Purens, 2016 [crinoids]; Dorit, 1990 [fishes]; Brochu & Sumrall, 2020 [crocodilians]; Pilbrow, 2010 [primates]). If these cases are representative of most taxa, the assumption is made that species in the fossil record (“morphospecies,” or species identified from morphology only) are equivalent to extant biological species (Allmon, 2016; Gould, 2002).…”