DOI: 10.5061/dryad.2bvq83bwf
title: A new Lower Permian ray-finned fish (Actinopterygii) from South Dakota and the use of tree space to find rogue taxa in phylogenetic analysis of morphological data

description: The divergence of extant lineages from the “palaeoniscoids”, a grade of
 Paleozoic and early Mesozoic Era species, remains unresolved in analyses
 of morphological data despite more than four decades of phylogenetic
 research. We describe a new ray-finned fish, Tenupiscis dakotaensis gen
 et. sp. nov., from the Lower Permian (Kungurian) of South Dakota to
 strengthen the phylogenetic framework of Mississippian–Triassic
 actinopterygians. Our initial parsimony and Bayesian phylogenetic analyses
 were unable to resolve the relationships of Mississippian–Triassic
 “palaeoniscoids”. We analyzed the topological variation among the trees
 sampled in each phylogenetic search (tree space) to determine if
 uncertainty was concentrated in a small subset of species with highly
 uncertain phylogenetic relationships relative to other terminal taxa
 (rogue taxa) or distributed evenly amongst early actinopterygians. The
 relationships of fourteen species were unresolved in the parsimony strict
 consensus due to a single rogue taxon (“Kalops monophyrum”). Parsimony and
 Bayesian analyses with the rogue pruned or recoded find the initially
 unresolved Mississippian–Triassic “palaeoniscoids” (including Tenupiscis)
 branching from the actinopterygian stem or from the base of
 Pan-Neopterygii . Our work supports the emerging consensus that Paleozoic
 Era ray-finned fishes therefore, include clades of stem actinopterygians
 and the earliest members of the actinopterygian crown group. We also
 demonstrate that tree space methods can effectively identify and mitigate
 rogue taxon effects in phylogenetic analysis of morphological data from
 new fossil taxa.
description: The input data in the form of phylogenetic trees were generated
 in a series of analyses (see "Parsimony analyses" and
 "Bayesian analyses" below). The matrices that we ran in the
 phylogenetics programs to make the trees are also provided (see
 "matrix construction" below). We used R to summarize the results
 of the Bayesian searches as majority rules consensus trees, visualized the
 variation in the samples of phylogenetic trees ("Tree space
 visualization), and determined if any rogue taxa were present in our
 dataset ("Rogue taxon search").
 <em>Summary of experimental efforts underlying this
 dataset</em>: This document describes a series of
 phylogenetic analyses of ray-finned fishes (actinopterygians) on the basis
 of a dataset of morphological data derived from Stack and Gottfried
 (2022). A full description of the methods is provided in the draft
 manuscript and supplementary information documents, but we will provide a
 summary of relevant information for how the data were generated and all
 information needed to replicate the analyses.
 <em>Matrix construction:</em> We
 coded the new taxon for 222 discrete morphological characters using the
 matrix of Stack & Gottfried (2022), which incorporates coding
 changes from Argyriou et al. (2018) and Coates & Tietjen (2019),
 adds the early Permian actinopterygian Concentrilepis minnekahtaensis, and
 reduces the taxon list to focus on actinopterygian interrelationships
 (Stack & Gottfried, 2022). The full list of changes is available
 in the supplementary material of Stack and Gottfried (2022). The full
 matrix used in our initial analyses contains 10341 scorings for 75 taxa;
 this matrix and analyses using it are labeled Initial below. We also used
 a matrix where the scorings for the terminal taxon "Kalops
 monophyrum" (nomen nudum, meaning a name that is not linked to a
 described taxon) were removed, referred to below as "Removed".
 The third and final version of the matrix replaces "Kalops
 monophyrum" with Kalops monophrys (Poplin & Lund, 2002), and
 is labeled as "Recoded". All parsimony and Bayesian analyses
 followed identical steps described below; the only difference between
 analyses was the matrices used. We used the open-access Morphobank
 (O'Leary and Kaufman 2011) to annotate and output all matrix
 files.  <em>Parsimony analyses:
 </em> All parsimony analyses were conducted in
 TNT 1.5 (Goloboff et al., 2008; Goloboff & Catalano, 2016) and
 implemented an initial New Technology Search with a combination of the
 Sectorial Search, Ratchet, Tree Fusing, and Drift algorithms to find the
 optimal tree length 500 times (random seed =1; Goloboff et al., 2008). We
 conducted a subsequent traditional search with Tree Bisection and
 Reconnection on the topologies returned from the New Technology search.
 Our strict consensus tree summarizes the agreement between the most
 parsimonious trees from the traditional search (Nixon & Carpenter,
 1996). We mapped unambiguous synapomorphies from the most parsimonious
 trees onto the strict consensus for each analysis in TNT. We also
 calculated Bremer support values in TNT by conducting Tree Bisection and
 Reconnection on the most parsimonious trees and allowing the analysis to
 retain all trees 1-6 steps longer than the optimal length. We calculated
 the consistency index (CI; Kluge and Farris, 1969) and retention index
 (RI; Farris, 1989) of the strict consensus in TNT with the stats.run
 command.  <em>Bayesian analyses:
 </em> All Bayesian phylogenetic analyses were
 conducted in MrBayes 3.7.2a using two independent Metropolis-coupled
 Markov chain Monte Carlo analyses with the MkV model for discrete
 morphological data (Ronquist et al., 2012; Lewis, 2001). Each
 Metropolis-coupled Markov chain Monte Carlo analysis had four independent
 Markov chains that ran for an initial 500,000 iterations, with burn-in set
 to 25% and sampling every 100 generations. We ran 4.5 million generations
 prior to reaching a standard deviation of split frequencies of 0.008282,
 with the minimum Effective Sample Size (ESS; Ripley, 1987) exceeding 6000
 and the Potential Scale Reduction Factor (PSRF; Gelman & Rubin,
 1992) values equaling 1.0. We also used the “plot” command in MrBayes to
 examine the trend in sampled log-likelihood values to ensure that they are
 randomly distributed within the space between generations 1,125,000 and
 4,500,000, indicating that the chains converged on a stable region of the
 posterior distribution. We generated majority rule consensus trees in
 MrBayes with the “sumt Burninfrac=0.5” command. We imported the nexus
 formatted consensus tree into R (R Core Team, 2021) to generate a more
 flexible annotated consensus tree with the ape (Paradis & Schliep,
 2019), phytools (Revell, 2012), and phylotate (Beer & Beer, 2019)
 packages, which we opened in Figtree v1.4.4 (Rambaut, 2018). 
 <em>Tree space visualization: </em>
 We applied a series of “tree space” techniques for visualizing
 variation in phylogenetic searches (Smith, 2022; Wright & Lloyd,
 2020) to examine the variation in our tree searches and determine the
 source of any low resolution in our consensus topologies. We conducted
 three parallel studies of tree space in R (R Core Team, 2021), with the
 cluster (Maechler et al., 2022), TreeTools (Smith, 2019), TreeDist (Smith,
 2020), vioplot (Adler & Kelly, 2022), ape (Paradis &
 Schliep, 2019), and protoclust (Bien & Tibshirani, 2022) packages.
 Our analyses are inspired by vignettes by Martin R. Smith
 (https://github.com/ms609/TreeDist/blob/HEAD/vignettes/treespace.Rmd;
 https://ms609.github.io/TreeDist/dev/articles/compare-treesets.html). We
 examined the most parsimonious trees and 1000 randomly sampled Bayesian
 trees on their own in addition to a separate analysis of the Bayesian and
 most parsimonious trees together. A full script and the files needed to
 recreate these analyses in R are provided in the Supplementary Data. We
 calculated the distance between trees via the clustering information
 distance metric, which Smith (2020) demonstrated to be the most consistent
 measure of tree dissimilarity among available metrics. See Smith (2020)
 for detailed comparisons and rigorous testing of measures of tree
 distance. We performed a principal coordinates analysis (PCoA or metric
 multidimensional scaling; Gower, 1966) of each tree sample to create a
 twelve-dimensional mapping of the distances between the topologies. We
 calculated the product of the trustworthiness and continuity (TxC; Kaski
 et al., 2003; Venna & Kaski, 2001) of mappings in 1-12 dimensions
 to determine how many dimensions were needed to reliably visually
 represent the distances between the topologies in each tree sample. The
 trustworthiness measures the degree to which proximities in the original
 distance matrix are preserved (Kaski et al., 2003), whereas continuity
 measures to what degree points that are nearby in the original matrix
 maintain proximity in the mapping (Smith, 2022; Venna & Kaski,
 2001). We mapped each tree space with the number of dimensions needed to
 meet or surpass a TxC of 0.9, following the recommendation of Smith
 (2022). We searched for clustering in each tree distance matrix via
 Partitioning Around Medoids (PAM; Kaufman & Rousseeuw, 1990; with
 algorithmic improvements from Schubert & Rousseeuw, 2021) and
 hierarchical clustering with minimax linkage (Hierarchical; Ao et al.,
 2005; Bien & Tibshirani, 2011) algorithms. We calculated the
 silhouette coefficient (Kaufman & Rousseeuw, 1990) to evaluate the
 reliability of the 2-12 clustering structures identified by each
 algorithm. The silhouette coefficient is a dimensionless measure of the
 degree to which objects in a cluster are close to other objects in their
 cluster relative to objects in the closest neighboring cluster (Kaufman
 & Rousseeuw, 1990). We further evaluated potential clustering by
 calculating and visualizing the dispersal of each tree sample, which is
 the distance between each tree and the respective median tree, to further
 understand the geometry of their respective tree spaces (Smith, 2022). The
 median tree has the shortest average distance from each other tree in the
 set (Smith, 2022). Examining the spread of the tree samples about their
 median allowed us to verify the landscapes shown in the initial tree space
 analyses. We visualized dispersal between and within the most parsimonious
 trees and Bayesian tree sample using violin plots (Adler & Kelly,
 2022) and density plots, based on a vignette by Tom Kelly, https://cran.r-project.org/web/packages/vioplot/vignettes/violin_area.html <em>Rogue taxon search: </em> In this context, rogue taxa are species with highly uncertain phylogenetic position relative to other species in the same analysis (Smith, 2021). We aimed to determine if any of these unstable taxa acted as rogues in our analysis by conducting a rogue taxon search with the R (R Core Team, 2021) package Rogue (Smith, 2021) on the most parsimonious trees and a sample of 1000 trees randomly sampled from the first run of the Bayesian analysis (accounting for a burn-in of 50%) with the QuickRogue function. We chose to use the Quickrogue function because it can identify rogues as reliably as alternative heuristics in Rogue and RogueNaRok (Aberer et al., 2013) with the benefit of lower computation time (Smith, 2021). The Rogue output shows the splitwise phylogenetic information content (the sum of the information content contained in the bipartitions of a topology; Smith, 2021) of the baseline majority rule consensus of the tree sample and the rawImprovement, which shows the change in phylogenetic information content for the removal of each rogue taxon. We compared the rawImprovement scores of each rogue to determine how much damage they caused relative to each other. We conducted follow-up maximum parsimony searches in TNT 1.5 with the sole rogue taxon (“Kalops monophyrum”) identified in the initial maximum parsimony analysis removed from the matrix. “Kalops monophyrum” is not one of the two described species of Kalops (Poplin & Lund, 2002) and is therefore a nomen nudum. Given the rogue behavior of this taxon, we opted to remove “Kalops monophyrum” and re-score Kalops based on personal examination of the type specimen of Kalops monophrys (Poplin & Lund, 2002; CM 27372) and the original description (Poplin & Lund, 2002). The rationale for each character coding change is provided in Part C of the Supplementary Information. We conducted an additional maximum parsimony analysis with “Kalops monophyrum” pruned from the matrix, along with a maximum parsimony analysis and Bayesian search with Kalops monophrys subbed in for “Kalops monophyrum”. These searches used identical phylogenetic search and tree space methods to the initial analyses.  References:<br>Aberer A.J., Krompass D., Stamatakis A. 2013. Pruning rogue taxa improves phylogenetic accuracy: An efficient algorithm and webservice. Systematic biology. 62(1):162–166. Adler D. Kelly, S.T. 2022. vioplot: violin plot. R package version 0.4.0 https://github.com/TomKellyGenetics/vioplot Argyriou T., Giles S., Friedman M., Romano C., Kogan I., Sánchez-Villagra M.R. 2018. Internal cranial anatomy of Early Triassic species of †Saurichthys (Actinopterygii: †Saurichthyiformes): Implications for the phylogenetic placement of †saurichthyiforms. BMC Evolutionary Biology. 18:1–41. Bien J., Tibshirani R. 2011. Hierarchical clustering with prototypes via minimax linkage. Journal of the American Statistical Association. 106:1075–1084. Bien J, Tibshirani R. 2022. Protoclust: Hierarchical clustering with prototypes. https://cranr-projectorg/web/packages/protoclust/indexhtml. Coates M.I., Tietjen K. 2019. ‘This strange little palaeoniscid': A new early actinopterygian genus, and commentary on pectoral fin conditions and function. Earth and Environmental Science Transactions of The Royal Society of Edinburgh. 109(1–2):15–31. Farris J.S. 1989. The retention index and the rescaled consistency index. Cladistics: the international journal of the Willi Hennig Society. 5(4):417–419. Gelman A., Rubin D.B. 1992. Inference from iterative simulation using multiple sequences. Statistical Science. 7(4):457-472. Goloboff P.A., Farris J.S., Nixon K.C. 2008. TNT, a free program for phylogenetic analysis. Cladistics. 24(5):774–786. Goloboff PA, Catalano SA. 2016. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics. 32(3):221–238. Gower JC. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 53(3-4):325-338. Kaski S., Nikkilä J., Oja M., Venna J., Törönen P., Castrén E. 2003. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics. 4:1–13. Kaufman L., Rousseeuw P.J. 1990. Partitioning around medoids (program PAM). Finding groups in data: An introduction to cluster analysis. Hoboken, New Jersey: John Wiley & Sons, Ltd. Kluge A.G., Farris J.S. 1969. Quantitative phyletics and the evolution of anurans. Systematic Biology. 18(1):1-32. Lewis P.O. 2001. A likelihood approach to estimating phylogeny from discrete morphological character data. Systematic biology. 50(6):913–925. Maechler M., Rousseeuw P., Struyf A., Hubert M., Hornik K. 2022. Cluster: Cluster analysis basics and extensions. R package version 2.1.3 2022. https://CRAN.R-project.org/package=cluster. Nixon K.C., Carpenter J.M. 1996. On consensus, collapsibility, and clade concordance. Cladistics. 12(4):305–321. O’Leary MA, Kaufman S. 2011. Morphobank: Phylophenomics in the “cloud”. Cladistics. 27(5):529-537. Ripley B.D. 2009. Stochastic simulation. John Wiley & Sons. Paradis E, Schliep K. 2019. Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 35:526–528. Poplin C.M., Lund R. 2002. Two Carboniferous fine-eyed palaeoniscoids (Pisces, Actinopterygii) from Bear Gulch (USA). Journal of Paleontology. 76:1014–1028. R (R Core Team 2021) is needed to run the code in Stacketal_SupplementCode. We recommend using Morphobank (Morphobank.org; O'Leary and Kaufman 2011) to open and read phylogenetic matrix files, although can be opened as text files. Re-running the phylogenetic analyses would require TNT 1.5 (parsimony; Goloboff et al., 2008; Goloboff & Catalano, 2016) and MrBayes 3.7.2a (Ronquist et al., 2012). Rambaut A. 2018. Figtree tree figure drawing tool version 1.4.4. https://githubcom/rambaut/figtree/releases. Ronquist F., Teslenko M., Van Der Mark P., Ayres D.L., Darling A., Höhna S., Larget B., Liu L., Suchard M.A., Huelsenbeck J.P. 2012. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic biology. 61(3):539–542. Schubert E., Rousseeuw P.J. 2021. Fast and eager k-medoids clustering: O (k) runtime improvement of the pam, clara, and clarans algorithms. Information Systems. 101:101804. Smith M. 2019. Treetools: Create, modify and analyse phylogenetic trees. Comprehensive R Archive Network. doi:10.5281/zenodo.3522725.  Smith MR. 2020. Information theoretic generalized Robinson–Foulds metrics for comparing phylogenetic trees. Bioinformatics. 36(20):5007–5013. Smith M.R. 2020. Treedist: Distances between phylogenetic trees. R package version 2.4.0. 2020. doi: 10.5281/zenodo.3528124. Smith M.R. 2021. Using information theory to detect rogue taxa and improve consensus trees. Systematic Biology 0:1–7. Smith M.R. 2022. Robust analysis of phylogenetic tree space. Systematic Biology. 0(syab099):1–16. Stack J, Gottfried M.D. 2022. A new, exceptionally well-preserved Permian actinopterygian fish from the Minnekahta Limestone of South Dakota, USA. Journal of Systematic Palaeontology. 19:1271–1302. Venna J., Kaski S. 2001. Neighborhood preservation in nonlinear projection methods: An experimental study. In: Dorffner G, Bischof H, Hornik K. editors. Artificial neural networks — ICANN 2001. Lecture notes in computer science. Berlin: Springer. p. 485–492. Wright A.M., Lloyd, G.T. 2020. Bayesian analyses in phylogenetic palaeontology: interpreting the posterior sample. Palaeontology 1–10.
description: # A new Lower Permian ray-finned fish (Actinopterygii) from South Dakota
 and the use of tree space to find rogue taxa in phylogenetic analysis of
 morphological data ## GENERAL INFORMATION Principal Investigator: Jack
 Stack (Virginia Tech, Blacksburg, VA, USA). Email:
 [Jackrs@vt.edu](mailto:Jackrs@vt.edu) Co-authors: Michael D. Gottfried
 (Michigan State, East Lansing, MI, USA); Michelle R. Stocker (Virginia
 Tech, Blacksburg, VA, USA). Date of project: 2021-2023. ## DATA & FILE
 OVERVIEW * StacketalSupplementData.zip 1\. File List: ### File Name:
 Stacketal_SupplementCode.r File Description: R script file that provides
 the full code needed to run the tree space visualizations, rogue taxon
 search, and annotation of the Bayesian consensus tree in R (R Core Team
 2021). These are each of the command line instructions we used, and an
 explanation of what the instruction is for. We attempt to make every step
 in our search for rogue taxa clear and repeatable, so
 Stacketal_SupplementCode.r contains a great deal of annotation, describing
 what each set of code does and is for. This code is written for a beginner
 R user, so the advanced user may find some of it obvious, but our goal is
 that our work is accessible beyond R-experts. ### Folder Name:
 Initial_Bayesian_Analysis ##### -&gt;Folder Name:
 Initial_Bayesian_Consensus_Tree File Name:
 Stacketal_MrBayesCon_Initial.tre File Description: Annotated majority rule
 consensus tree from MrBayes, which is the input for creating an annotated
 consensus in R. This tree serves as the result for the initial Bayesian
 search. This tree shows the arrangements with the highest estimated
 posterior probability that surpass a value of 0.5. If no arrangement for a
 taxon has an estimated posterior probability above 0.5, it is represented
 as uncertain (a polytomy). File Name: BayesianConsensus_FigTre File
 Description: Plain text file of the majority rule consensus tree from the
 initial Bayesian search that can be opened in Figtree v1.4.4 (Rambaut,
 2018). ##### -&gt;Folder Name: MrBayes_Script File Name:
 Initial_MrBayes_Script.rtf File Description: Raw text formatted script to
 run the initial Bayesian search in MrBayes. These are step-by-step
 command-line instructions to replicate the steps of our analysis in
 MrBayes. Relationship to other files: Requires
 "Stacketal_InitialMatrix.nex". ##### -&gt;Folder Name:
 Initial_Bayesian_Tree_Search File Name: Stacketal_InitialMatrix.nex File
 Description: Nexus formatted matrix for a Bayesian phylogenetic analysis
 in MrBayes 3.7.2a. In this context, a matrix describes the phylogenetic
 character annotations for the species of interest. File Name:
 Stacketal_InitialBayes_Runonetrees.t File Description: Nexus formatted
 file containing the trees sampled from our initial Bayesian search. File
 Name: InitialBayes_TxCofPCOA.pdf File Description: PDF file of a plot
 trustworthiness x continuity showing the reliability (the product of
 trustworthiness x continuity) of 1-12 dimensions in the PCoA space of the
 subsampled Bayesian trees. File Name: InitialBayes_PCoA_Visualization.pdf
 File Description: PDF formatted plot of the PCoA space from 1000
 phylogenetic trees sampled from the initial Bayesian search. This plot
 shows the dissimilarity of the phylogenetic trees in a low-dimensional (7
 in this instance)Euclidean space (Gower, 1966). File Name:
 InitialBayes_ClusterSearch.pdf File Description: PDF of a comparison of
 the reliability(silhouette coefficient; Kaufman & Rousseeuw, 1990) of
 the clusters identified via Partitioning Around Medoids (PAM; Kaufman
 & Rousseeuw, 1990; with algorithmic improvements from Schubert &
 Rousseeuw (2021) and hierarchical clustering with minimax linkage
 (Hierarchical; Ao et al., 2005; Bien & Tibshirani, 2011) algorithms.
 The silhouette coefficient is a dimensionless measure of the degree to
 which objects in a cluster are close to other objects in their cluster
 relative to objects in the closest neighboring cluster (Kaufman &
 Rousseeuw, 1990). File Name: InitialBayes_DensityPlot.pdf File
 Description: PDF of a plot of the density of the subsampled initial
 Bayesian trees about the median tree. The density refers to the distance
 of each tree from the median tree (Smith, 2022). ### Folder Name:
 Kalops_Recoded_BayesianAnalysis ##### -&gt;Folder Name: MrBayesScript File
 Name: Kalops_Recoded_MrBayes_Script.rtf File Description: Raw text
 formatted script for the Bayesian analysis with "Kalops
 monophyrum" recoded as Kalops monophrys in MrBayes. These are
 step-by-step command-line instructions to replicate each step of our
 analysis. These instructions include how to read
 "Stacketal_KalopsRecodedMatrix_Bayes.nex" into MrBayes, which is
 described below. ##### -&gt;Folder Name: Recoded_Bayesian_Tree_Search File
 Name: Stacketal_KalopsRecodedMatrix_Bayes.nex File Description: Nexus
 formatted matrix with "Kalops monophyrum" recoded as Kalops
 monophrys for Bayesian analysis in MrBayes 3.7.2a. In this context, a
 matrix describes the phylogenetic character annotations for the species of
 interest. File Name: Stacketal_KalopsRecoded_Runonetrees.t File
 Description: Nexus formatted tree file containing the trees sampled in the
 Bayesian search in MrBayes 3.7.2a. with "Kalops monophyrum"
 recoded as Kalops monophrys. File Name: RecodedBayes_TxCofPCOA.pdf File
 Description: PDF trustworthiness x continuity plot (Kaski et al., 2003;
 Venna & Kaski, 2001) showing the reliability (the product of
 trustworthiness x continuity) of 1-12 dimensions in the Principal
 Coordinates Analysis space of the subsampled Bayesian trees. File Name:
 RecodedBayes_PCoA_Visualization.pdf" File Description: PDF file of a
 plot of the PCoA space from the subsampled Bayesian trees. This plot shows
 the dissimilarity of the phylogenetic trees in a low-dimensional Euclidean
 space (Gower, 1966). File Name: InitialBayes_ClusterSearch.pdf File
 Description: PDF of a comparison of the reliability (silhouette
 coefficient; Kaufman & Rousseeuw, 1990) of the clusters identified via
 Partitioning Around Medoids (PAM; Kaufman & Rousseeuw, 1990; with
 algorithmic improvements from Schubert & Rousseeuw (2021) and
 hierarchical clustering with minimax linkage (Hierarchical; Ao et al.,
 2005; Bien & Tibshirani, 2011) algorithms. The silhouette coefficient
 is a dimensionless measure of the degree to which objects in a cluster are
 close to other objects in their cluster relative to objects in the closest
 neighboring cluster (Kaufman & Rousseeuw, 1990). File Name:
 RecodedBayes_DensityPlot.pdf File Description: PDF of a plot the density
 of the subsampled initial Bayesian trees about the median tree. The
 density refers to the distance of each tree from the median tree (Smith,
 2022). ### Folder Name: Initial_ParsimonyAnalysis ##### -&gt;Folder Name:
 Matrix File Name: Initial_Parsimony_TNTMatrix.tnt File Description: A .tnt
 formatted text file of a phylogenetic matrix matrix formatted for analysis
 in TNT by Morphobank(O'Leary and Kaufman 2011). In this context, a
 matrix is a text file that describes the phylogenetic character
 annotations for the species of interest. ##### -&gt;Folder Name:
 Initial_Parsimony_Tree_Search File Name: Initial_Parsimony_TNTReader.tnt
 File Description: A text file needed for R to read the most parsimonious
 trees as output by TNT. Because TNT annotates species in tree files as
 numbers rather than their names, R needs a file to translate the numbers
 back to species names. This is a text file that is identical to
 "Initial_Parsimony_TNTMatrix.tnt". File Name:
 Initial_Parsimony_Synapomorphies.emf File Description: A .emf formatted
 image file showing the strict consensus phylogenetic of the initial
 parsimony search annotated with the unambiguous synapomorphies for each
 branching pattern. In this context, an unambiguous synapomorphy refers to
 shared characteristics for the phylogenetic relationship represented by
 the tree pattern. File Name: Initial_Parsimony_MPT.tre File Description: A
 .tre file of the most parsimonious trees from the initial maximum
 parsimony search output by TNT 1.5. In this context, a .tre file is a text
 file that describes phylogenetic tree structure. File Name:
 Initial_MPT_Readable File Description: A .tre text file of the most
 parsimonious trees from TNT that are written with the species annotated as
 their names, rather than numbers (as is the TNT standard). Unlike the TNT
 output, this file can be read more easily by R and other programs for
 examining phylogenetic trees. File Name: InitialParsimony_TxCofPCOA.pdf
 File Description: A PDF formatted trustworthiness x continuity plot (Kaski
 et al., 2003; Venna & Kaski, 2001) showing the reliability (the
 product of trustworthiness x continuity) of 1-12 dimensions in the
 Principal Coordinates Analysis space of the most parsimonious trees from
 the initial search in TNT 1.5. File Name: InitialParsimony_3DPCOA.pdf File
 Description: A PDF-formatted plot of the PCoA space of the most
 parsimonious trees. This plot shows the dissimilarity of the phylogenetic
 trees in a low-dimensional (3 in this instance) Euclidean space (Gower,
 1966). File Name: InitialParsimony_ClusterSearch.pdf File Description: A
 PDF formatted plot of a comparison of the reliability (silhouette
 coefficient; Kaufman & Rousseeuw, 1990) of the clusters identified via
 Partitioning Around Medoids (PAM; Kaufman & Rousseeuw, 1990; with
 algorithmic improvements from Schubert & Rousseeuw (2021) and
 hierarchical clustering with minimax linkage (Hierarchical; Ao et al.,
 2005; Bien & Tibshirani, 2011) algorithms. The silhouette coefficient
 is a dimensionless measure of the degree to which objects in a cluster are
 close to other objects in their cluster relative to objects in the closest
 neighboring cluster (Kaufman & Rousseeuw, 1990). File Name:
 InitialParsimony_ClusterOneConsensus.pdf File Description: PDF formatted
 file describing the strict consensus of the trees in the first cluster of
 the most parsimonious trees. This strict consensus tree shows the species
 arrangements that are present in 100% of the trees in the first cluster.
 File Name: InitialParsimony_ClusterTwoConsensus.pfg File Description: PDF
 formatted file describing the strict consensus of the trees in the second
 cluster of the most parsimonious trees. This strict consensus tree shows
 the species arrangements that are present in 100% of the trees in the
 second cluster. File Name: InitialParsimony_DispersalDensityPlot.pdf File
 Description: PDF formatted file showing a plot showing the dispersal of
 the most parsimonious trees about their median. The density refers to the
 distance of each tree from the median tree (Smith, 2022). File Name:
 Initial_Parsimony_StrictCon.tre File Description: Text file describing the
 strict consensus of the most parsimonious trees from the initial maximum
 parsimony search. This strict consensus tree shows the species
 arrangements that are present in 100% of the most parsimonious trees. ###
 Folder Name: Kalops_Removed_ParsimonyAnalysis ##### -&gt;Folder Name:
 Matrix File Name: KalopsRemoved_TNTMAtrix.tnt File Description: Text file
 of a phylogenetic matrix formatted for analysis in TNT by Morphobank
 (O'Leary and Kaufman 2011). In this context, a matrix describes the
 phylogenetic character annotations for the species of interest. #####
 -&gt;Folder Name: Kalops_Removed_Tree_Search File Name:
 Kalops_Removed_MPT.tre File Description: A .tre text file of the 190 most
 parsimonious trees from the maximum parsimony search of the initial matrix
 with "Kalops monophyrum" removed in TNT 1.5. File Name:
 Kalops_RemovedReaderFile.tnt File Description: A text file needed for R to
 read the most parsimonious trees as output by TNT. Because TNT annotates
 species in tree files as numbers rather than their names, R needs a file
 to translate the numbers back to species names. This file is a
 text-formatted matrix identical in content to
 "KalopsRemoved_TNTMAtrix.tnt". File Name:
 KalopsRemoved_MPT_Readable File Description: A .tre text file of the most
 parsimonious trees from TNT that are written with the species annotated as
 their names, rather than numbers (as is the TNT standard). Unlike the TNT
 output, this file can be read more easily by R and other programs for
 examining phylogenetic trees. File Name:
 KalopsRemoved_Parsimony_TxCofPCOA.pdf File Description: A PDF formatted
 file of a trustworthiness x continuity plot showing the reliability (the
 product of trustworthiness x continuity) of the dimensions in the PCoA
 space of the most parsimony trees from the maximum parsimony analysis with
 "Kalops monophyrum" removed. File Name:
 KalopsRemoved_Synapomorphies.emf File Description: A .emf formatted image
 file showing the strict consensus phylogenetic of the parsimony search
 with "Kalops monophyrum" removed from the matrix, annotated with
 the unambiguous synapomorphies for each branching pattern. In this
 context, an unambiguous synapomorphy refers to shared characteristics for
 the phylogenetic relationship represented by the tree pattern. File Name:
 KalopsRemoved_Parsimony_PCoA_Visualization.pdf File Description: A PDF
 formatted plot of a 4-dimensional visualization of the PCoA space of the
 most parsimony trees from the maximum parsimony analysis with "Kalops
 monophyrum" removed. This plot shows the dissimilarity of the
 phylogenetic trees in a low-dimensional (4 in this instance) Euclidean
 space (Gower, 1966). File Name: KalopsRemoved_Parsimony_ClusterSearch.pdf
 File Description: A PDF formatted plot of a comparison of the reliability
 (silhouette coefficient) the clusters identified via Partitioning Around
 Medoids(PAM; Kaufman & Rousseeuw, 1990; with algorithmic improvements
 from Schubert & Rousseeuw(2021) and hierarchical clustering with
 minimax linkage (Hierarchical; Ao et al., 2005; Bien & Tibshirani,
 2011) algorithms. The silhouette coefficient is a dimensionless measure of
 the degree to which objects in a cluster are close to other objects in
 their cluster relative to objects in the closest neighboring cluster
 (Kaufman & Rousseeuw, 1990). File Name:
 KalopsRemoved_Parsimony_DensityPlot.pdf File Description: PDF formatted
 plot of the density of the most parsimonious trees from the maximum
 parsimony analysis with "Kalops monophyrum" removed from the
 matrix about their median. The density refers to the distance of each tree
 from the median tree (Smith, 2022). File Name:
 KalopsRemoved_Strictconsensus.tre File Description: A .tre text file of
 the strict consensus of the most parsimonious trees from the maximum
 parsimony search of the initial matrix with "Kalops monophyrum"
 removed. This strict consensus tree shows the species arrangements that
 are present in 100% of the most parsimonious trees. ### Folder Name:
 Combined_Bayesian_Parsimony_TreeSpace File Name:
 Initial_Parsimony_TNTReader.tnt File Description: A .tnt formatted text
 file needed for R to read the most parsimonious trees as output by TNT.
 Because TNT annotates species in tree files as numbers rather than their
 names, R needs a file to translate the numbers back to species names. This
 is a text file that is identical to
 "Initial_Parsimony_TNTMatrix.tnt". File Name:
 Initial_Parsimony_MPT.tre File Description: A .tre formatted text file of
 the most parsimonious trees from the initial maximum parsimony search in
 TNT. File Name: Stacketal_InitialBayes_Runonetrees.t File Description: A
 Nexus-formatted text file containing the trees sampled in our initial
 Bayesian search. File Name: CombinedTreeSpace_2DPlot.pdf File Description:
 A PDF formatted plot of the first two dimensions of a principal
 coordinates analysis of the combined tree space of the most parsimonious
 trees and a sample of trees from the estimated Bayesian posterior
 distribution. This plot shows the dissimilarity of the phylogenetic trees
 in a low-dimensional (2 in this instance) Euclidean space (Gower, 1966).
 File Name: CombinedTreeSpace_5DPlot.pdf File Description: A PDF formatted
 plot of the first five dimensions of a principal coordinates analysis of
 the combined tree space of the most parsimonious trees and a sample of
 trees from the estimated Bayesian posterior distribution. This plot shows
 the dissimilarity of the phylogenetic trees in a low-dimensional (5 in
 this instance) Euclidean space (Gower, 1966). File Name:
 CombinedTreeSample_ViolinPlot.pdf File Description: A PDF formatted plot
 showing the dispersal of the most parsimonious and Bayesian tree sample as
 a violin plot. The dispersal refers to the distance of each tree from the
 median tree (Smith, 2022). File Name: MostParsimoniousTree_DensityPlot.pdf
 File Description: A PDF-formatted plot that shows the dispersal of the
 most parsimonious trees about their median tree. The density refers to the
 distance of each tree from the median tree (Smith, 2022). File Name:
 BayesianTrees_DensityPlot.pdf File Description: A PDF-formatted plot that
 shows the dispersal of the Bayesian tree sample about its median tree. The
 density refers to the distance of each tree from the median tree (Smith,
 2022). ### Folder Name: Kalops_Recoded_ParsimonyAnalysis ##### -&gt;Folder
 Name: Matrix File Name: KalopsRecoded.tnt File Description: A TNT
 formatted text file of the phylogenetic matrix used for the maximum
 parsimony analysis where "Kalops monophyrum" is replaced with
 Kalops monphrys. Matrix created in Morphobank (O'Leary and Kaufman
 2011). In this context, a matrix describes the phylogenetic character
 annotations for the species of interest. ##### -&gt;Folder Name:
 Kalops_Recoded_Tree_Search File Name: KalopsRecoded_MPT.tre File
 Description: A .tre formatted text file of the TNT output of the most
 parsimonious trees from the maximum parsimony analysis of where
 "Kalops monophyrum" is replaced with Kalops monphrys. File Name:
 KalopsRecoded.tnt File Description: A .tnt formatted text file needed for
 R to read the most parsimonious trees as output by TNT. Because TNT
 annotates species in tree files as numbers rather than their names, R
 needs a file to translate the numbers back to species names. This is a
 text file that is identical to "KalopsRecoded.tnt". File Name:
 KalopsRecoded_MPT_Readable File Description: A .tre formatted text file of
 the most parsimonious trees from TNT that are written with the species
 annotated as their names, rather than numbers (as is the TNT standard).
 Unlike the TNT output, this file can be read more easily by R and other
 programs for examining phylogenetic trees. File Name:
 KalopsRecoded_Synapomorphies.emf File Description: A .emf formatted image
 file showing the strict consensus phylogenetic of the parsimony search
 with "Kalops monophyrum" recoded as Kalops monophrys from the
 matrix, annotated with the unambiguous synapomorphies for each branching
 pattern. In this context, an unambiguous synapomorphy refers to shared
 characteristics for the phylogenetic relationship represented by the tree
 pattern. File Name: KalopsRecoded_Parsimony_TxCofPCOA.pdf File
 Description: A PDF formatted file of a trustworthiness x continuity plot
 showing the reliability of the dimensions in the PCoA space of the most
 parsimony trees from the maximum parsimony analysis with "Kalops
 monophyrum" recoded as Kalops monophrys. File Name:
 KalopsRecoded_Parsimony_PCoA_Visualization.pdf File Description: A PDF
 formatted file of a 2-dimensional visualization of the PCoA space of the
 most parsimony trees from the maximum parsimony analysis with "Kalops
 monophyrum" recoded as Kalops monophrys. This plot shows the
 dissimilarity of the phylogenetic trees in a low-dimensional (2 in this
 instance) Euclidean space (Gower, 1966). File Name:
 KalopsRecoded_Parsimony_ClusterSearch.pdf File Description: A
 PDF-formatted file of a plot comparison of the reliability (silhouette
 coefficient) of the clusters identified in each cluster search. File Name:
 KalopsRecoded_Parsimony_DensityPlot.pdf File Description: A PDF formatted
 plot of the density of the most parsimonious trees from the maximum
 parsimony analysis with "Kalops monophyrum" recoded as Kalops
 monophrys. File Name: KalopsRecoded_Strictconsensus.tre File Description:
 A .tre formatted text file of the strict consensus of the most
 parsimonious trees from the maximum parsimony search of the initial matrix
 with "Kalops monophyrum" recoded as Kalops monophrs. References:
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