10.5061/DRYAD.27QJ6
Monti, Daniela S.
CIUDAD
Confalonieri, Viviana A.
CIUDAD
Data from: Comparing phylogenetics and linear morphometrics to solve the
generic assignment of Parabolinella? triarthroides Harrington (Trilobita,
Olenidae)
Dryad
dataset
2017
2020-06-26T00:00:00Z
en
https://doi.org/10.1017/jpa.2017.60
635153 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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