> ## this script takes semistric data and applies MrBayes utility to build Bayesian tree > ## requires MrBayes installation and working "mb-mpi > > library(shipunov) package 'shipunov', version 1.5-1 > library(ape) > DATE <- format(Sys.time(), "%Y%m%d_%H%M%S") # timestamp > treesp <- read.table("_kubricks_treesp.txt", sep="\t", h=TRUE, as.is=TRUE) # to understand outgroups and outliers > outgroups <- treesp$SPECIES.NEW[treesp$TYPE == "outgroup" & treesp$USE == 1] > outgroups <- gsub(" ", "_", outgroups) > conc <- read.dna("40_concatenated/semistrict.fasta", format="fasta") > LAB <- sub("__.*$", "", labels(conc)) > OUT <- labels(conc)[LAB %in% outgroups] > outliers <- treesp$SPECIES.NEW[treesp$TYPE == "outlier" & treesp$USE == 1] > if (length(outliers) > 0) { + outliers <- gsub(" ", "_", outliers) + EXC <- labels(conc)[LAB %in% outliers] + conc <- conc[!labels(conc) %in% EXC, ] # remove outliers, if any + } > setwd("80_mrbayes_working") # go to MrBayes working directory > tr <- MrBayes(conc, file="semistrict", exec="mb-mpi", # change MrBayes binary if needed, on Windows, you _need_ to change it + ngen=1e+04, # change MrBayes options if needed + run=TRUE) # default is not run, just make a NEXUS file for MrBayes #NEXUS [created by ips on Sat Feb 29 14:13:33 2020] begin data; dimensions ntax=12 nchar=1119; format datatype=dna missing=N gap=-; matrix Kubrickus_heus__K-008 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtagga-gaaaatccgttggctttatagaccgtgagaactggcctcaaatcaggtaggactacccgctgaacttaagcatatcaataagcggaggaaaagaaactaacaaggattcccctagtaacggcgagtgaagcgggaagagctcaaatttgaaatctggtggcctcaggtcatccgagttgtaatctatagaagtgttttccgtgctggct-catgtac--aagtcccttggaacagggcgtcatagagggtgagaatcccgtccttgacatgaactaccagtgctct-------gtgatacattttcaacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaattccatctaaagctaaatattggcgagagaccgatagcgccaaccca-cccc Kubrickus_aus__K-001_efgh_- --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Kubrickus_beus__K-002_efgh_- --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Kubrickus_ceus__K-003_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Kubrickus_deus__K-004 aggataataacattgcatttgcaatgcagaaataatataatgattaccagccagtcatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatgcttcgggtgtggcc-ccggtct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagctaaatattggccagagaccgatagcgacaaca-cccccc Kubrickus_eus__K-005 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatctaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatgcttcgggtgtggcc-ccggtct--aggttccttggaacaggacgtcatagagggtgagaatcccgtatgtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagctaaatattggccagagaccgatagcgacaaca-cccccc Kubrickus_feus__K-006_efgh_XYZ124 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatccttcgggtgtggcc-ccggtctggaggttccttggaacaggacgtcatagagggtgagaatcccgt--gtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagct--atattggccagagaccgatagcgacacaa-cccca- Kubrickus_geus__K-007_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgacagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Kubrickus_ieus__K-009_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn Kubrickus_jeus__K-010 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgacagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagtcgtttgggaatgcagctc-taatgtg-agtaaatttcttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca Kubrickus_keus__K-011 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgaaagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagttgtttgggaatgcagctc-taatggg-agtaaatttcttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca Kubrickus_leus__K-012 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagttgtttgggaatgcagctc-taatggg-agtaaattttttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca ; end; MrBayes v3.2.6 x64 (Bayesian Analysis of Phylogeny) (Parallel version) (2 processors available) Distributed under the GNU General Public License Type "help" or "help " for information on the commands that are available. Type "about" for authorship and general information about the program. Executing file "semistrict" UNIX line termination Longest line length = 1155 Parsing file Expecting NEXUS formatted file Reading data block Allocated taxon set Allocated matrix Defining new matrix with 12 taxa and 1119 characters Data is Dna Missing data coded as N Gaps coded as - Taxon 1 -> Kubrickus_heus__K-008 Taxon 2 -> Kubrickus_aus__K-001_efgh_- Taxon 3 -> Kubrickus_beus__K-002_efgh_- Taxon 4 -> Kubrickus_ceus__K-003_efgh_- Taxon 5 -> Kubrickus_deus__K-004 Taxon 6 -> Kubrickus_eus__K-005 Taxon 7 -> Kubrickus_feus__K-006_efgh_XYZ124 Taxon 8 -> Kubrickus_geus__K-007_efgh_- Taxon 9 -> Kubrickus_ieus__K-009_efgh_- Taxon 10 -> Kubrickus_jeus__K-010 Taxon 11 -> Kubrickus_keus__K-011 Taxon 12 -> Kubrickus_leus__K-012 Successfully read matrix Setting default partition (does not divide up characters) Setting model defaults Seed (for generating default start values) = 1582953214 Setting output file names to "semistrict.run." Exiting data block Reading mrbayes block Setting Nst to 6 Setting Rates to Invgamma Setting Ngammacat to 4 Successfully set likelihood model parameters Setting number of runs to 2 Setting number of generations to 10000 Setting print frequency to 100 Setting sample frequency to 10 Setting number of chains to 4 Setting heating parameter to 0.200000 Setting chain output file names to "semistrict.run.

" Running Markov chain MCMC stamp = 7524774487 Seed = 1005929022 Swapseed = 1582953214 Model settings: Data not partitioned -- Datatype = DNA Nucmodel = 4by4 Nst = 6 Substitution rates, expressed as proportions of the rate sum, have a Dirichlet prior (1.00,1.00,1.00,1.00,1.00,1.00) Covarion = No # States = 4 State frequencies have a Dirichlet prior (1.00,1.00,1.00,1.00) Rates = Invgamma The distribution is approximated using 4 categories. Likelihood summarized over all rate categories in each generation. Shape parameter is exponentially distributed with parameter (1.00). Proportion of invariable sites is uniformly dist- ributed on the interval (0.00,1.00). Active parameters: Parameters --------------------- Revmat 1 Statefreq 2 Shape 3 Pinvar 4 Ratemultiplier 5 Topology 6 Brlens 7 --------------------- 1 -- Parameter = Revmat Type = Rates of reversible rate matrix Prior = Dirichlet(1.00,1.00,1.00,1.00,1.00,1.00) 2 -- Parameter = Pi Type = Stationary state frequencies Prior = Dirichlet 3 -- Parameter = Alpha Type = Shape of scaled gamma distribution of site rates Prior = Exponential(1.00) 4 -- Parameter = Pinvar Type = Proportion of invariable sites Prior = Uniform(0.00,1.00) 5 -- Parameter = Ratemultiplier Type = Partition-specific rate multiplier Prior = Fixed(1.0) 6 -- Parameter = Tau Type = Topology Prior = All topologies equally probable a priori Subparam. = V 7 -- Parameter = V Type = Branch lengths Prior = Unconstrained:GammaDir(1.0,0.1000,1.0,1.0) Number of chains per MPI processor = 4 The MCMC sampler will use the following moves: With prob. Chain will use move 0.93 % Dirichlet(Revmat) 0.93 % Slider(Revmat) 0.93 % Dirichlet(Pi) 0.93 % Slider(Pi) 1.85 % Multiplier(Alpha) 1.85 % Slider(Pinvar) 9.26 % ExtSPR(Tau,V) 9.26 % ExtTBR(Tau,V) 9.26 % NNI(Tau,V) 9.26 % ParsSPR(Tau,V) 37.04 % Multiplier(V) 12.96 % Nodeslider(V) 5.56 % TLMultiplier(V) Division 1 has 83 unique site patterns Initializing conditional likelihoods Using standard SSE likelihood calculator for division 1 (single-precision) Initializing invariable-site conditional likelihoods Initial log likelihoods and log prior probs for run 1: Chain 1 -- -2771.124336 -- 42.620562 Chain 2 -- -2716.857739 -- 42.620562 Chain 3 -- -2881.576235 -- 42.620562 Chain 4 -- -2738.556069 -- 42.620562 There are 4 more chains on the other processor Using a relative burnin of 25.0 % for diagnostics Chain results (10000 generations requested): 0 -- [-2771.124] (-2716.858) (-2881.576) (-2738.556) [...4 remote chains...] 100 -- (-2383.057) (-2372.321) [-2358.153] (-2405.709) [...4 remote chains...] -- 0:00:00 200 -- (-2302.387) [-2272.942] (-2293.187) (-2303.654) [...4 remote chains...] -- 0:00:00 300 -- (-2273.977) (-2246.896) [-2247.789] (-2281.203) [...4 remote chains...] -- 0:00:00 400 -- (-2268.257) [-2235.761] (-2244.889) (-2272.900) [...4 remote chains...] -- 0:00:00 500 -- (-2258.833) (-2241.123) [-2221.521] (-2263.295) [...4 remote chains...] -- 0:00:00 600 -- (-2259.834) (-2232.494) [-2217.589] (-2253.882) [...4 remote chains...] -- 0:00:00 700 -- (-2265.463) (-2231.658) [-2219.727] (-2248.213) [...4 remote chains...] -- 0:00:00 800 -- (-2248.340) (-2229.645) [-2218.271] (-2241.454) [...4 remote chains...] -- 0:00:00 900 -- (-2255.552) (-2230.408) [-2214.836] (-2233.075) [...4 remote chains...] -- 0:00:00 1000 -- (-2255.369) [-2222.359] (-2216.838) (-2233.827) [...4 remote chains...] -- 0:00:00 1100 -- (-2242.912) [-2222.194] (-2225.357) (-2236.741) [...4 remote chains...] -- 0:00:00 1200 -- (-2245.289) (-2219.750) (-2221.226) [-2228.853] [...4 remote chains...] -- 0:00:00 1300 -- (-2240.249) [-2219.460] (-2222.640) (-2229.209) [...4 remote chains...] -- 0:00:00 1400 -- (-2239.562) [-2217.631] (-2220.214) (-2234.610) [...4 remote chains...] -- 0:00:00 1500 -- (-2231.749) [-2220.099] (-2224.680) (-2227.203) [...4 remote chains...] -- 0:00:00 1600 -- (-2227.318) (-2222.698) [-2227.342] (-2235.646) [...4 remote chains...] -- 0:00:00 1700 -- (-2221.688) (-2220.357) (-2221.398) [-2218.413] [...4 remote chains...] -- 0:00:00 1800 -- (-2222.162) (-2217.466) [-2211.275] (-2221.446) [...4 remote chains...] -- 0:00:00 1900 -- (-2227.325) (-2215.727) (-2215.232) [-2225.253] [...4 remote chains...] -- 0:00:00 2000 -- (-2229.489) (-2216.439) [-2212.487] (-2229.980) [...4 remote chains...] -- 0:00:00 2100 -- (-2221.504) (-2218.118) (-2216.634) [-2218.467] [...4 remote chains...] -- 0:00:00 2200 -- (-2223.721) [-2212.924] (-2215.173) (-2231.333) [...4 remote chains...] -- 0:00:00 2300 -- (-2223.005) [-2210.666] (-2206.907) (-2219.800) [...4 remote chains...] -- 0:00:00 2400 -- (-2226.506) [-2214.140] (-2207.102) (-2216.210) [...4 remote chains...] -- 0:00:00 2500 -- (-2224.416) [-2209.934] (-2205.697) (-2213.977) [...4 remote chains...] -- 0:00:00 2600 -- (-2219.647) [-2212.594] (-2207.736) (-2216.012) [...4 remote chains...] -- 0:00:00 2700 -- (-2220.375) (-2209.339) [-2205.144] (-2216.678) [...4 remote chains...] -- 0:00:00 2800 -- (-2222.688) [-2213.957] (-2206.775) (-2211.540) [...4 remote chains...] -- 0:00:00 2900 -- (-2227.200) (-2219.084) (-2211.640) [-2215.036] [...4 remote chains...] -- 0:00:00 3000 -- (-2222.219) (-2215.579) [-2205.416] (-2215.810) [...4 remote chains...] -- 0:00:00 3100 -- (-2225.985) (-2224.688) [-2195.950] (-2224.362) [...4 remote chains...] -- 0:00:00 3200 -- (-2228.005) (-2213.211) [-2203.684] (-2219.681) [...4 remote chains...] -- 0:00:00 3300 -- (-2226.545) (-2221.826) [-2199.496] (-2219.793) [...4 remote chains...] -- 0:00:00 3400 -- (-2231.160) [-2210.326] (-2200.023) (-2217.070) [...4 remote chains...] -- 0:00:00 3500 -- (-2230.143) (-2215.871) (-2205.317) [-2209.671] [...4 remote chains...] -- 0:00:00 3600 -- (-2229.558) (-2218.752) [-2198.801] (-2215.178) [...4 remote chains...] -- 0:00:00 3700 -- (-2231.747) (-2212.128) [-2205.745] (-2213.719) [...4 remote chains...] -- 0:00:00 3800 -- (-2231.701) (-2214.989) [-2205.450] (-2215.544) [...4 remote chains...] -- 0:00:00 3900 -- (-2226.519) (-2214.661) (-2206.311) [-2207.515] [...4 remote chains...] -- 0:00:00 4000 -- (-2227.687) (-2212.305) [-2207.236] (-2211.254) [...4 remote chains...] -- 0:00:00 4100 -- (-2226.434) [-2217.730] (-2211.853) (-2211.619) [...4 remote chains...] -- 0:00:00 4200 -- (-2219.460) [-2211.290] (-2202.443) (-2212.377) [...4 remote chains...] -- 0:00:00 4300 -- (-2218.558) (-2207.241) [-2201.829] (-2215.528) [...4 remote chains...] -- 0:00:00 4400 -- (-2217.736) (-2214.035) [-2203.650] (-2216.661) [...4 remote chains...] -- 0:00:00 4500 -- (-2214.425) (-2213.642) [-2206.203] (-2207.944) [...4 remote chains...] -- 0:00:00 4600 -- (-2224.126) (-2218.506) (-2211.844) [-2212.875] [...4 remote chains...] -- 0:00:00 4700 -- (-2230.270) [-2209.951] (-2208.691) (-2215.092) [...4 remote chains...] -- 0:00:00 4800 -- (-2227.887) (-2210.280) [-2206.588] (-2211.816) [...4 remote chains...] -- 0:00:00 4900 -- (-2230.940) [-2204.757] (-2205.956) (-2212.312) [...4 remote chains...] -- 0:00:00 5000 -- (-2219.978) [-2204.439] (-2207.286) (-2217.236) [...4 remote chains...] -- 0:00:00 Average standard deviation of split frequencies: 0.061481 5100 -- (-2216.345) (-2204.033) [-2209.804] (-2209.585) [...4 remote chains...] -- 0:00:00 5200 -- (-2221.051) [-2205.580] (-2215.802) (-2204.511) [...4 remote chains...] -- 0:00:00 5300 -- (-2225.298) [-2195.218] (-2210.013) (-2208.972) [...4 remote chains...] -- 0:00:00 5400 -- (-2231.389) [-2197.442] (-2221.221) (-2205.241) [...4 remote chains...] -- 0:00:00 5500 -- (-2233.397) [-2199.226] (-2220.772) (-2205.409) [...4 remote chains...] -- 0:00:00 5600 -- (-2226.507) [-2199.614] (-2217.998) (-2207.983) [...4 remote chains...] -- 0:00:00 5700 -- (-2226.327) [-2197.571] (-2218.096) (-2210.174) [...4 remote chains...] -- 0:00:00 5800 -- (-2223.883) [-2199.572] (-2215.344) (-2209.479) [...4 remote chains...] -- 0:00:00 5900 -- (-2226.900) [-2199.864] (-2212.863) (-2216.056) [...4 remote chains...] -- 0:00:00 6000 -- (-2225.782) [-2200.524] (-2218.419) (-2217.728) [...4 remote chains...] -- 0:00:00 6100 -- (-2231.717) [-2200.286] (-2213.978) (-2214.404) [...4 remote chains...] -- 0:00:00 6200 -- (-2226.132) [-2200.896] (-2205.508) (-2219.192) [...4 remote chains...] -- 0:00:00 6300 -- (-2223.805) [-2204.844] (-2208.911) (-2217.060) [...4 remote chains...] -- 0:00:00 6400 -- (-2223.326) [-2202.726] (-2204.878) (-2216.653) [...4 remote chains...] -- 0:00:00 6500 -- (-2224.699) [-2205.479] (-2212.596) (-2214.634) [...4 remote chains...] -- 0:00:00 6600 -- (-2227.419) [-2199.915] (-2211.627) (-2219.467) [...4 remote chains...] -- 0:00:00 6700 -- (-2220.570) (-2208.481) [-2203.745] (-2228.153) [...4 remote chains...] -- 0:00:00 6800 -- (-2219.162) [-2200.033] (-2201.607) (-2210.306) [...4 remote chains...] -- 0:00:00 6900 -- (-2227.271) (-2200.452) [-2198.719] (-2216.419) [...4 remote chains...] -- 0:00:00 7000 -- (-2215.432) [-2203.140] (-2201.594) (-2213.939) [...4 remote chains...] -- 0:00:00 7100 -- (-2220.540) (-2206.670) [-2200.403] (-2217.383) [...4 remote chains...] -- 0:00:00 7200 -- (-2218.670) [-2202.027] (-2207.599) (-2214.649) [...4 remote chains...] -- 0:00:00 7300 -- (-2218.408) [-2212.488] (-2206.458) (-2217.891) [...4 remote chains...] -- 0:00:00 7400 -- (-2213.730) [-2207.775] (-2210.282) (-2217.004) [...4 remote chains...] -- 0:00:00 7500 -- (-2213.103) [-2206.540] (-2209.469) (-2220.920) [...4 remote chains...] -- 0:00:00 7600 -- (-2217.260) [-2202.697] (-2206.090) (-2220.164) [...4 remote chains...] -- 0:00:00 7700 -- (-2213.114) [-2204.830] (-2207.898) (-2216.292) [...4 remote chains...] -- 0:00:00 7800 -- (-2222.592) [-2208.285] (-2206.016) (-2222.545) [...4 remote chains...] -- 0:00:00 7900 -- (-2219.878) [-2205.508] (-2221.416) (-2221.526) [...4 remote chains...] -- 0:00:00 8000 -- (-2209.222) (-2218.874) [-2212.746] (-2215.200) [...4 remote chains...] -- 0:00:00 8100 -- (-2217.085) (-2209.575) [-2206.261] (-2210.196) [...4 remote chains...] -- 0:00:00 8200 -- (-2208.824) (-2217.770) (-2205.465) [-2205.456] [...4 remote chains...] -- 0:00:00 8300 -- [-2208.022] (-2213.092) (-2208.136) (-2213.600) [...4 remote chains...] -- 0:00:00 8400 -- (-2210.061) (-2214.718) (-2203.606) [-2213.503] [...4 remote chains...] -- 0:00:00 8500 -- (-2208.436) (-2215.118) [-2198.055] (-2213.414) [...4 remote chains...] -- 0:00:00 8600 -- [-2201.826] (-2209.755) (-2204.270) (-2217.296) [...4 remote chains...] -- 0:00:00 8700 -- (-2210.144) (-2218.115) [-2206.291] (-2213.281) [...4 remote chains...] -- 0:00:00 8800 -- (-2212.395) (-2215.419) [-2205.856] (-2210.960) [...4 remote chains...] -- 0:00:00 8900 -- [-2202.247] (-2209.077) (-2206.486) (-2215.793) [...4 remote chains...] -- 0:00:00 9000 -- (-2206.786) (-2212.253) [-2200.503] (-2213.866) [...4 remote chains...] -- 0:00:00 9100 -- (-2209.686) (-2210.854) [-2206.431] (-2213.892) [...4 remote chains...] -- 0:00:00 9200 -- [-2211.126] (-2208.937) (-2215.171) (-2209.550) [...4 remote chains...] -- 0:00:00 9300 -- (-2205.449) (-2212.711) (-2209.399) [-2209.335] [...4 remote chains...] -- 0:00:00 9400 -- [-2201.742] (-2215.367) (-2213.201) (-2216.808) [...4 remote chains...] -- 0:00:00 9500 -- [-2198.975] (-2212.760) (-2212.839) (-2221.615) [...4 remote chains...] -- 0:00:00 9600 -- [-2198.164] (-2213.166) (-2210.907) (-2218.351) [...4 remote chains...] -- 0:00:00 9700 -- (-2200.033) [-2207.892] (-2206.640) (-2212.555) [...4 remote chains...] -- 0:00:00 9800 -- (-2200.310) (-2210.881) [-2210.337] (-2212.054) [...4 remote chains...] -- 0:00:00 9900 -- (-2201.485) (-2216.307) [-2205.735] (-2208.511) [...4 remote chains...] -- 0:00:00 10000 -- (-2200.246) (-2213.404) [-2205.623] (-2213.224) [...4 remote chains...] -- 0:00:00 Average standard deviation of split frequencies: 0.050891 Analysis completed in 1 second Analysis used 1.78 seconds of CPU time on processor 0 Likelihood of best state for "cold" chain of run 1 was -2194.89 Likelihood of best state for "cold" chain of run 2 was -2197.80 Acceptance rates for the moves in the "cold" chain of run 1: With prob. (last 100) chain accepted proposals by move NA NA Dirichlet(Revmat) NA NA Slider(Revmat) NA NA Dirichlet(Pi) NA NA Slider(Pi) 82.7 % ( 81 %) Multiplier(Alpha) 96.2 % ( 95 %) Slider(Pinvar) 16.4 % ( 19 %) ExtSPR(Tau,V) 15.5 % ( 18 %) ExtTBR(Tau,V) 18.8 % ( 27 %) NNI(Tau,V) 7.1 % ( 10 %) ParsSPR(Tau,V) 73.3 % ( 72 %) Multiplier(V) 50.2 % ( 48 %) Nodeslider(V) 27.3 % ( 25 %) TLMultiplier(V) Acceptance rates for the moves in the "cold" chain of run 2: With prob. (last 100) chain accepted proposals by move NA NA Dirichlet(Revmat) NA NA Slider(Revmat) NA NA Dirichlet(Pi) NA NA Slider(Pi) 86.2 % ( 85 %) Multiplier(Alpha) 90.4 % ( 92 %) Slider(Pinvar) 14.2 % ( 22 %) ExtSPR(Tau,V) 13.1 % ( 15 %) ExtTBR(Tau,V) 19.1 % ( 21 %) NNI(Tau,V) 7.1 % ( 7 %) ParsSPR(Tau,V) 72.5 % ( 66 %) Multiplier(V) 53.0 % ( 55 %) Nodeslider(V) 23.8 % ( 23 %) TLMultiplier(V) Chain swap information for run 1: 1 2 3 4 -------------------------- 1 | 0.43 0.17 0.06 2 | 1654 0.49 0.22 3 | 1695 1635 0.59 4 | 1692 1663 1661 Chain swap information for run 2: 1 2 3 4 -------------------------- 1 | 0.49 0.22 0.07 2 | 1725 0.55 0.26 3 | 1693 1645 0.57 4 | 1654 1630 1653 Upper diagonal: Proportion of successful state exchanges between chains Lower diagonal: Number of attempted state exchanges between chains Chain information: ID -- Heat ----------- 1 -- 1.00 (cold chain) 2 -- 0.83 3 -- 0.71 4 -- 0.62 Heat = 1 / (1 + T * (ID - 1)) (where T = 0.20 is the temperature and ID is the chain number) Setting sumt filename and outputname to semistrict Setting urn-in to 10 Setting sumt contype to Allcompat Setting sumt conformat to Simple Summarizing trees in files "semistrict.run1.t" and "semistrict.run2.t" Using relative burnin ('relburnin=yes'), discarding the first 25 % of sampled trees Writing statistics to files semistrict. Examining first file ... Found one tree block in file "semistrict.run1.t" with 1001 trees in last block Expecting the same number of trees in the last tree block of all files Tree reading status: 0 10 20 30 40 50 60 70 80 90 100 v-------v-------v-------v-------v-------v-------v-------v-------v-------v-------v ********************************************************************************* Read a total of 2002 trees in 2 files (sampling 1502 of them) (Each file contained 1001 trees of which 751 were sampled) General explanation: In an unrooted tree, a taxon bipartition (split) is specified by removing a branch, thereby dividing the species into those to the left and those to the right of the branch. Here, taxa to one side of the removed branch are denoted '.' and those to the other side are denoted '*'. Specifically, the '.' symbol is used for the taxa on the same side as the outgroup. In a rooted or clock tree, the tree is rooted using the model and not by reference to an outgroup. Each bipartition therefore corresponds to a clade, that is, a group that includes all the descendants of a particular branch in the tree. Taxa that are included in each clade are denoted using '*', and taxa that are not included are denoted using the '.' symbol. The output first includes a key to all the bipartitions with frequency larger or equual to (Minpartfreq) in at least one run. Minpartfreq is a parameter to sumt command and currently it is set to 0.10. This is followed by a table with statistics for the informative bipartitions (those including at least two taxa), sorted from highest to lowest probability. For each bipartition, the table gives the number of times the partition or split was observed in all runs (#obs) and the posterior probability of the bipartition (Probab.), which is the same as the split frequency. If several runs are summarized, this is followed by the minimum split frequency (Min(s)), the maximum frequency (Max(s)), and the standard deviation of frequencies (Stddev(s)) across runs. The latter value should approach 0 for all bipartitions as MCMC runs converge. This is followed by a table summarizing branch lengths, node heights (if a clock model was used) and relaxed clock parameters (if a relaxed clock model was used). The mean, variance, and 95 % credible interval are given for each of these parameters. If several runs are summarized, the potential scale reduction factor (PSRF) is also given; it should approach 1 as runs converge. Node heights will take calibration points into account, if such points were used in the analysis. Note that Stddev may be unreliable if the partition is not present in all runs (the last column indicates the number of runs that sampled the partition if more than one run is summarized). The PSRF is not calculated at all if the partition is not present in all runs.The PSRF is also sensitive to small sample sizes and it should only be considered a rough guide to convergence since some of the assumptions allowing one to interpret it as a true potential scale reduction factor are violated in MrBayes. List of taxa in bipartitions: 1 -- Kubrickus_heus__K-008 2 -- Kubrickus_aus__K-001_efgh_- 3 -- Kubrickus_beus__K-002_efgh_- 4 -- Kubrickus_ceus__K-003_efgh_- 5 -- Kubrickus_deus__K-004 6 -- Kubrickus_eus__K-005 7 -- Kubrickus_feus__K-006_efgh_XYZ124 8 -- Kubrickus_geus__K-007_efgh_- 9 -- Kubrickus_ieus__K-009_efgh_- 10 -- Kubrickus_jeus__K-010 11 -- Kubrickus_keus__K-011 12 -- Kubrickus_leus__K-012 Key to taxon bipartitions (saved to file "semistrict.parts"): ID -- Partition ------------------ 1 -- .*********** 2 -- .*.......... 3 -- ..*......... 4 -- ...*........ 5 -- ....*....... 6 -- .....*...... 7 -- ......*..... 8 -- .......*.... 9 -- ........*... 10 -- .........*.. 11 -- ..........*. 12 -- ...........* 13 -- ....*...*... 14 -- .........**. 15 -- .**.******** 16 -- .......*.*** 17 -- .......*...* 18 -- ....***.*... 19 -- ..*....*.*** 20 -- ....*.*.*... 21 -- ..*...*..... 22 -- .*..***.*... 23 -- .**.***.*... 24 -- ..*.***.*... 25 -- ..*..**..... 26 -- .......*.**. 27 -- ..*.******** 28 -- .....**..... 29 -- .........*** 30 -- .**....*.*** 31 -- .*.....*.*** 32 -- .*...*...... ------------------ Summary statistics for informative taxon bipartitions (saved to file "semistrict.tstat"): ID #obs Probab. Sd(s)+ Min(s) Max(s) Nruns ---------------------------------------------------------------- 13 1420 0.945406 0.005649 0.941411 0.949401 2 14 1292 0.860186 0.035779 0.834887 0.885486 2 15 1260 0.838881 0.086623 0.777630 0.900133 2 16 1258 0.837550 0.030130 0.816245 0.858855 2 17 906 0.603196 0.020714 0.588549 0.617843 2 18 726 0.483356 0.126168 0.394141 0.572570 2 19 659 0.438748 0.072500 0.387483 0.490013 2 20 584 0.388815 0.077207 0.334221 0.443409 2 21 538 0.358189 0.043311 0.327563 0.388815 2 22 487 0.324234 0.046136 0.291611 0.356858 2 23 483 0.321571 0.106396 0.246338 0.396804 2 24 447 0.297603 0.080032 0.241012 0.354194 2 25 342 0.227696 0.015065 0.217044 0.238349 2 26 335 0.223036 0.017890 0.210386 0.235686 2 27 239 0.159121 0.072500 0.107856 0.210386 2 28 223 0.148469 0.006591 0.143808 0.153129 2 29 188 0.125166 0.037662 0.098535 0.151798 2 30 147 0.097870 0.032954 0.074567 0.121172 2 31 109 0.072570 0.055552 0.033289 0.111851 2 32 104 0.069241 0.048961 0.034621 0.103862 2 ---------------------------------------------------------------- + Convergence diagnostic (standard deviation of split frequencies) should approach 0.0 as runs converge. Summary statistics for branch and node parameters (saved to file "semistrict.vstat"): 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median PSRF+ Nruns -------------------------------------------------------------------------------------- length[1] 0.026670 0.000238 0.000458 0.053421 0.026134 1.001 2 length[2] 0.001473 0.000002 0.000000 0.004044 0.001048 1.073 2 length[3] 0.001574 0.000002 0.000016 0.004380 0.001172 1.051 2 length[4] 0.001473 0.000002 0.000006 0.005024 0.000864 1.021 2 length[5] 0.002986 0.000003 0.000399 0.006841 0.002621 1.008 2 length[6] 0.001801 0.000002 0.000018 0.004483 0.001480 1.003 2 length[7] 0.004259 0.000005 0.000346 0.008024 0.003937 1.001 2 length[8] 0.001927 0.000005 0.000050 0.006060 0.001351 1.008 2 length[9] 0.001455 0.000002 0.000006 0.003801 0.001110 1.031 2 length[10] 0.003401 0.000003 0.000698 0.006717 0.003201 1.000 2 length[11] 0.001710 0.000002 0.000023 0.004312 0.001405 1.001 2 length[12] 0.001779 0.000002 0.000007 0.004219 0.001525 1.005 2 length[13] 0.002195 0.000002 0.000134 0.005377 0.001915 1.001 2 length[14] 0.002714 0.000004 0.000063 0.007091 0.002302 1.021 2 length[15] 0.022319 0.000175 0.000179 0.046181 0.021275 1.000 2 length[16] 0.022023 0.000101 0.002060 0.038118 0.023971 1.018 2 length[17] 0.002418 0.000002 0.000212 0.005462 0.002149 1.011 2 length[18] 0.010195 0.000037 0.000257 0.020812 0.009751 1.017 2 length[19] 0.016605 0.000103 0.000626 0.037008 0.015569 0.999 2 length[20] 0.001661 0.000001 0.000177 0.003886 0.001410 0.999 2 length[21] 0.003275 0.000004 0.000651 0.007466 0.002934 0.998 2 length[22] 0.009687 0.000030 0.000160 0.018485 0.009842 1.020 2 length[23] 0.010064 0.000028 0.001403 0.018402 0.010176 1.225 2 length[24] 0.008511 0.000032 0.000057 0.019797 0.007958 1.083 2 length[25] 0.001487 0.000002 0.000048 0.003516 0.001074 1.011 2 length[26] 0.001524 0.000001 0.000111 0.003312 0.001316 1.001 2 length[27] 0.008354 0.000060 0.000248 0.024078 0.006263 1.173 2 length[28] 0.001346 0.000001 0.000055 0.003334 0.001091 0.998 2 length[29] 0.007620 0.000075 0.000058 0.027692 0.004605 1.008 2 length[30] 0.009314 0.000097 0.000259 0.029522 0.005824 1.219 2 length[31] 0.008069 0.000024 0.000831 0.016315 0.006107 1.029 2 length[32] 0.000930 0.000001 0.000047 0.002464 0.000711 0.994 2 -------------------------------------------------------------------------------------- + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman and Rubin, 1992) should approach 1.0 as runs converge. NA is reported when deviation of parameter values within all runs is 0 or when a parameter value (a branch length, for instance) is not sampled in all runs. Summary statistics for partitions with frequency >= 0.10 in at least one run: Average standard deviation of split frequencies = 0.050891 Maximum standard deviation of split frequencies = 0.126168 Average PSRF for parameter values (excluding NA and >10.0) = 1.032 Maximum PSRF for parameter values = 1.225 Clade credibility values: /---------------------------------------------------------- Kubrickus_heus_~ (1) | |---------------------------------------------------------- Kubrickus_ceus_~ (4) | | /--------------------------------------- Kubrickus_aus__~ (2) | | | | /---------- Kubrickus_deus_~ (5) + | /---95---+ | /---32---+ | \---------- Kubrickus_ieus_~ (9) | | | /----39---+ | | | | \------------------- Kubrickus_feus_~ (7) | | \----48---+ | | \----------------------------- Kubrickus_eus__~ (6) \----84---+ | /----------------------------- Kubrickus_beus_~ (3) | | | | /---------- Kubrickus_geus_~ (8) \--------44--------+ /---60---+ | | \---------- Kubrickus_leus~ (12) \----84---+ | /---------- Kubrickus_jeus~ (10) \---86---+ \---------- Kubrickus_keus~ (11) Phylogram (based on average branch lengths): /----------------------- Kubrickus_heus_~ (1) | |- Kubrickus_ceus_~ (4) | | /- Kubrickus_aus__~ (2) | | | | /-- Kubrickus_deus_~ (5) + | /-+ | /-------+ | \- Kubrickus_ieus_~ (9) | | | /+ | | | |\--- Kubrickus_feus_~ (7) | | \--------+ | | \- Kubrickus_eus__~ (6) \------------------+ | /- Kubrickus_beus_~ (3) | | | | /- Kubrickus_geus_~ (8) \------------+ /-+ | | \- Kubrickus_leus~ (12) \--------------------+ | /--- Kubrickus_jeus~ (10) \-+ \- Kubrickus_keus~ (11) |-------| 0.010 expected changes per site Calculating tree probabilities... Credible sets of trees (402 trees sampled): 50 % credible set contains 41 trees 90 % credible set contains 252 trees 95 % credible set contains 327 trees 99 % credible set contains 387 trees Exiting mrbayes block Reached end of file Tasks completed, exiting program because mode is noninteractive To return control to the command line after completion of file processing, set mode to interactive with 'mb -i ' (i is for interactive) or use 'set mode=interactive' > setwd("..") > tr <- tr[[1]] # we need the first tree > tr <- root(tr, outgroup=OUT, resolve.root=TRUE) > tr$node.label <- suppressWarnings(round(as.numeric(tr$node.label)*100)) # warning is OK so it is suppressed > ## plot tree into PDF > pdf(paste0("99_trees/", DATE, "_semistrict_mb_kubricks.pdf"), height=8, width=12) # change PDF size if needed > oldpar <- par(mar=rep(0, 4)) > plot(tr) > nodelabels(tr$node.label, frame="none", bg="transparent", adj=-0.1) > mtext("semistrict MB, all compatible to 50% majority rule", font=2, line=-1) > tmp <- legend("bottom", plot=FALSE, legend="") # this is how to get rid of overlapped scale bar > add.scale.bar(x=tmp$text$x, y=tmp$text$y) # it is now centered > dev.off() null device 1 > ## also save it into Newick > tr$node.label[tr$node.label == "NA"] <- "" # useful for some Newick reading software > write.tree(tr, file=paste0("99_trees/", DATE, "_semistrict_mb_kubricks.tre"))