DOI: 10.5061/dryad.27qj6
title: Data from: Comparing phylogenetics and linear morphometrics to solve the generic assignment of Parabolinella? triarthroides Harrington (Trilobita, Olenidae)

description: The use of different methodological approaches together with an exhaustive
 qualitative study has helped to recognize important morphological traits
 to distinguish species in a systematic and phylogenetic framework.
 Parabolinella triarthroides Harrington, 1938 was described based on two
 cranidia from the Quebrada de Coquena, Purmamarca, Jujuy province. The
 generic assignment of P. triarthroides has been questioned by a
 phylogenetic analysis, which resolves this species as the sister group of
 Bienvillia Clark, 1924. To explore the generic assignment of this species,
 a revision of the type material, plus a morphometric analysis including
 specimens of Parabolinella Brøgger, 1882 and Bienvillia were performed. In
 addition, the original matrix used in the published phylogeny was reviewed
 and enlarged, including more species of Bienvillia. Continuous characters
 were coded in different ways in order to compare how they could affect the
 ordering of specimens and their phylogenetic relationships. Finally, both
 methodologies were compared, especially in regard to the behavior of the
 quantitative characters included in the analyses. From the combined
 analyses, it is shown that similarities between the cranidium of P.
 triarthroides and all other Parabolinella species are true homologies
 instead of a by-product of evolutionary convergence. Therefore, P.
 triarthroides should be considered a member of this genus. Finally, this
 study demonstrates that the best strategy for solving systematic problems
 in groups where the morphological variation is the only source of
 information (i.e., fossil taxa without living representatives) is the
 implementation of an integrative approach, combining different
 methodological techniques and a good description of specimens.
description: Supplementary data set
 2Supplementary data set
 2. Measurement of morphological variables taken from the specimens
 included in the morphometric analyses. Acronyms as Table
 1Supplementary data set
 3Supplementary data set
 3. Scatter plot of the two first components of the PCAs obtained with the
 correlation matrices of untransformed data (1) Analysis using the raw
 data, (2) Analysis using the data corrected by the geometric mean (GMD),
 (3) Analysis using the ratio variables (RD). Green circles: Bienvillia
 Clark; blue circles: Parabolinella Brogger; Black star: Holotype of P?.
 triarthroides (CPBA 5); grey star P?. triarthroides (CPBA 54); red
 circles: P.? triarthroides.Supplementary data set
 4Supplementary data set
 4. Results of the PCAs performed form the correlation matrices of
 untransformed data: (1) Analysis using the raw data, (2) Analysis using
 the data corrected by the geometric mean (GMD), (3) Analysis using the
 ratio variables (RD). See Supplemental Data 2. Acronyms as Table 1.
 Asterisks indicate significative correlation.Supplementary data set
 5Supplementary data set
 5. Input data matrix of 23 species and 40 characters (15 continuous and 25
 qualitative). Each partition is indicated separately. (1) Continuous
 Partition: ratios (CPR), (2) Continuous Partition: Geometric Mean (CPGM),
 (3) Qualitative Partition (DP). Numbers of characters (0–39) and states
 for the qualitative partition as Table 2.Supplementary data set
 1Supplementary data set
 1. List of the specimens reviewed to construct the matrix for the
 phylogenetic analysis. *: Specimens whose measurements were included in
 the phylogenetic analysis, **: Specimens whose measurements were included
 in both phylogenetic and morphometric analyses.Supplementary data set
 6Supplementary data set
 6. Pairwise comparisons of the trees obtained with the different analyses
 and treatments of continuous characters, (1-2) Complete matrix (continuous
 partition + discrete partition): (1) SPR Distance, (2) Number of
 coincident nodes in the strict consensus; (3-4) Continuous Partition (CPGM
 vs. CPR): (3) SPR Distance, (4) Number of coincident nodes in the strict
 consensus; (5-6) Discrete partition and Continuous partition separately
 (CPGM and CPR vs. DP): (5) SPR Distance, (6) Number of coincident nodes in
 the strict consensus. T= topology