Monti and Confalonieri—Comparing the use of phylogenetics and morphometrics in systematics
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states in each continuous character has the same cost as one step in a discrete character). Because the arbitrary election of the numerator and denominator of the ratio character may affect the results obtained, the ratio variables were transformed with logarithm to minimize this effect (Mongiardino Koch et al., 2015). Continuous (CPR or CPGM) and discrete (DP) partitions
Figure 1. Schematic representation of the cranidium of Parabolinella sp. showing the measurements used in the morphometric analyses. Abbreviations: LOR, Length of the Occipital Ring (sag.); LC, Length of the Cranidium (sag.); Lgl, Length of the glabella (sag.); LPL, Length of the Palpebral Lobes (exasag.); WOR, Width of the Occipital Ring (tr.); Wgl.B, Width of the glabella (at the Base) (tr.); WPF, Width of the Posterior Fixigenae (tr.); WIG, Width of the Interocular Genae (tr.); Wgl.E, Width of the glabella (at the Eye line) (tr.); LPA, Length of the Preglabellar Area (sag.); LPF, Length of the Preglabellar Field (sag.).
estimated by one thousand replicates of bootstrap PCA with R package bootSVD version 0.5 (Fisher, 2015). To compare the effectiveness of both GMD and RD transformations in reducing size effects, the first axis of the PCA was correlated with two variables that estimate size (Log of the Length of the Cephalon [LC] and Log of the Geometric Mean [GM]). Finally, a Discriminant Analysis was conducted for the two genera, excluding the types of P.? triarthroides. In order to classify the type specimens ofP.? triarthroides, the values of the discriminant function for these specimens were calculated with the Microsoft Excel spreadsheet using canonical coefficients. These last two analyses were carried out with Infostat version 2015 (Di Rienzo et al., 2015).
Cladistic analysis.—The matrix used in the phylogenetic analysis of the genus Parabolinella (Monti and Confalonieri, 2013) was reviewed, especially the characters coded for P.? triarthroides. Two new taxa belonging to the genus Bienvillia (B. kicka and B. jana) were added. The matrix has 40 characters, of which fifteen are continuous and the rest are discrete. In the original matrix (Monti and Confalonieri, 2013), the continuous characters were coded as discrete (12 were expressed as ratios and three as meristic), but in the present study they were coded as continuous, as implemented in TNT (Goloboff et al., 2006). Two strategies were used for coding these continuous characters (Table 2, Supplementary dataset 5): the first one considers their original definition and characters are coded as ratios (hereafter called CPR); the second one considers the use of the raw data corrected for size by the geometric mean (hereafter CPGM) (Mosimann size variables) (see Mosimann and James, 1979; Meachen-Samuels and Van Valkenburgh, 2009). In both stra- tegies the median was used as the statistical descriptor because it has the advantage of being less affected by extreme values of frequency distribution. Also, to solve the scaling problem, the continuous characters were standardized to range 0–1, so the maximum internal steps are equivalent to one step of a discretely coded character (i.e., a change between the two most dissimilar
were used combined and separated in the cladistic analyses (Table 2, Supplementary dataset 5). Heuristic searches were performed with TNT (Goloboff et al., 2008), using random addition sequences (RAS) followed by tree bisection–reconnec- tion branch swapping (TBR). One thousand replicates were carried out, saving 25 trees per replicate. Maximum parsimony and implied weighting (concavity constants from k=3to k=14) were used as optimality criteria. The trees obtained with the different types of coding were compared using unweighted SPR distances (Goloboff, 2007) and the number of coincident internal nodes in the strict consensus tree. These values were taken to represent the degree of discordance between each continuous dataset and the discrete partition. Additionally, the mean value of these measurements was interpreted as the average topological difference resulted from choosing a different way to code the continuous characters. Branch support was calculated by means of Bremer
Support values (BS) (Bremer, 1994) through the search of suboptimal trees by TBR swapping. Also, jacknife resampling (Lanyon, 1985) was calculated using TNT (see Goloboff et al., 2003). One thousand jacknife replicates were performed, conducting a heuristic tree search consisting of 10 replicates of Wagner trees (with random addition sequences) followed by TBR (saving 10 trees per replicate). A probability of alteration equal to 10% (equivalent to four characters in this database) was used. The differences in GC frequencies (for Group present/ Contradicted) are also shown because they give better measures for groups with low support (Goloboff et al., 2003). Because the list of synapomorphies depends on the optimal
solution, when more than one most parsimonious tree was obtained, the common synapomorphies of all trees were considered on the strict consensus. Those quantitative characters that were important to distinguish both genera in the morpho- metric analysis were optimized on trees in order to analyze their phylogenetic signal, and to compare both kinds of analyses (i.e., morphometric vs. phylogenetic analyses).
Repository and institutional abbreviation.—Studied specimens are deposited in the Invertebrate Paleontology Collection, Department of Geology (Paleontology area), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, under repository abbreviations CPBA.
Systematic paleontology Class Trilobita Walch, 1771
Order Ptychopariida Swinnerton, 1915 Suborder Olenina Burmeister, 1843 Family Olenidae Burmeister, 1843 Subfamily Oleninae Burmeister, 1843 Genus Parabolinella Brøgger, 1882
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