DOI: 10.5061/dryad.qrfj6q5cg
title: A new perspective on the taxonomy and systematics of Arvicolinae (Gray, 1821) and a new time-calibrated phylogeny for the clade

description: Background. Arvicoline rodents are one of the most speciose and rapidly
 evolving mammalian lineages. Fossil arvicolines are also among the most
 common vertebrate fossils found in sites of Pliocene and Pleistocene age
 in Eurasia and North America. However, there is no taxonomically robust,
 well-supported, time-calibrated phylogeny for the group. 
 Methods. Here we present well-supported hypotheses of arvicoline
 rodent systematics using maximum likelihood and Bayesian inference of DNA
 sequences of two mitochondrial genes and three nuclear genes representing
 146 (82% coverage) species and 100% of currently recognized arvicoline
 genera. We elucidate well-supported major clades, reviewed the
 relationships and taxonomy of many species and genera, and critically
 compared our resulting molecular phylogenetic hypotheses to previously
 published hypotheses. We also used five fossil calibrations to generate a
 time-calibrated phylogeny of Arvicolinae that permitted some
 reconciliation between paleontological and neontological data. 
 Results. Our results are largely congruent with previous molecular
 phylogenies, but we increased the support in many regions of the
 arvicoline tree that were previously poorly-sampled. Our sampling resulted
 in a better understanding of relationships within Clethrionomyini, the
 early-diverging position and close relationship of true lemmings (Lemmus
 and Myopus) and bog lemmings (Synaptomys), and provided support for recent
 taxonomic changes within Microtini. Our results indicate an origin of ~6.4
 Ma for crown arvicoline rodents. These results have major implications
 (e.g., diversification rates, paleobiogeography) for our confidence in the
 fossil record of arvicolines and their utility as biochronological tools
 in Eurasia and North America during the Quaternary.
description: This dataset is a portion of a chapter of Charles Withnell's
 doctoral dissertation at The University of Texas at Austin and an article
 published by PeerJ. Molecular data was obtained from GenBank (we did not
 sequence any genetic data ourselves). This resulted in three separate
 datasets; (1) a dataset of only taxa with mitochondrial data (n=146), (2)
 a dataset of only taxa with nuclear data (n=107), (3) a concatenated
 dataset that includes both mitochondrial and nuclear loci of n=146 species
 of extant arvicolines. Maximum Likelihood was computed in RAxML v8.2.12
 (Stamatakis, 2014), and Bayesian Inference in MrBayes 3.2.7 (Ronquist et
 al., 2012). All analyses were conducted on the Cipres Cluster. Five fossil
 calibrations were used to constrain the phylogeny produced in MrBayes
 using all five genes.
description: All script should produce results. Note: Trying to run this
 script on different machines with different software updates can prove
 difficult. All analyses were conducted on a MacBook running the MacOS
 Monterey. 
description: # A new perspective on the taxonomy and systematics of Arvicolinae (Gray,
 1821) and a new time-calibrated phylogeny for the clade
 This dataset contains
 molecular data from five mitochondrial and nuclear genes of 146 species of
 arvicoline rodent and 3 outgroups. Maximum-likelihood analysis was
 completed in RAxML v8.2.12 (Stamatakis, 2014) and Bayesian Inference was
 computed in MrBayes 3.2.7 (Ronquist et al., 2012) on the Cipres Cluster.
 Divergence-dating using five fossil calibrations was also completed in
 MrBayes 3.2.7 (Ronquist et al., 2012). ## **Description of the data and
 file structure** Included in this dataset are all of the input files
 needed to reproduce our analyses. **RAxML_parameters_Cipres.txt** 1.    
 Parameters inputed into Cipres for the RAxML analysis. From top to bottom:
 Mitochondrial only, Nuclear only, Combined Mitochondrial and Nuclear data.
 **Age_prior_Evolutionary_rate_code.R** 1.     At the beginning is “R” code
 for producing the prior distribution for the age of a calibrated node. * A
 new user should only change myOffset (the minimum age of a node), and
 myMedian (median of exponential distribution of the age prior). 2.    
 Further down is the code derived from Gunnell et al. (2018) for
 calculating the evolutionary rate for an uncalibrated tree. This was done
 for the mitochondrial only, nuclear only, and combined dataset. * You will
 need a newick file of your tree of interest and a text file of dates. If
 all taxa are modern than you will use an age of “0”. However, if fossils
 are used you would put the age of the fossil. **combined_newick.nwk**
 1.     Newick file of the combined mitochondrial and nuclear dataset that
 you would use in your calculation of the evolutionary rate from the file
 Age_prior_Evolutionary_rate_code.R. **nuclear_newick.nwk** 1.     Newick
 file of the nuclear dataset that you would use in your calculation of the
 evolutionary rate from the file Age_prior_Evolutionary_rate_code.R.
 **mito_newick.nwk** 1.     Newick file of the mitochondrial dataset that
 you would use in your calculation of the evolutionary rate from the file
 Age_prior_Evolutionary_rate_code.R. **nuclear_dates.txt** 1.     Text file
 of the nuclear dataset ages that you would use in your calculation of the
 evolutionary rate from the file Age_prior_Evolutionary_rate_code.R.
 **mito_dates.txt** 1.     Text file of the mitochondrial dataset ages that
 you would use in your calculation of the evolutionary rate from the file
 Age_prior_Evolutionary_rate_code.R. **combined_dates.txt** 1.     Text
 file of the combined mitochondrial and nuclear dataset ages that you would
 use in your calculation of the evolutionary rate from the file
 Age_prior_Evolutionary_rate_code.R. **Combined_RAxML_inputfile.txt**
 1.     Text file with the combined mitochondrial and nuclear dataset used
 in the RAxML analysis on Cipres. **Mitochondrial_RAxML_inputfile.txt**
 1.     Text file with the mitochondrial dataset used in the RAxML analysis
 on Cipres. **Nuclear_RAxML_inputfile.txt** 1.     Text file with the
 nuclear dataset used in the RAxML analysis on Cipres.
 **Appendix_A_FINAL.xlsx** 1.     Table with all species included in the
 analysis 2.     Missing data = total number of nucleotides missing from
 the complete dataset (n=5857) 3.     Columns H-L = GenBank accession
 numbers 4.     Columns M-Q = Voucher number * Blank spaces = no specimen
 and no voucher * NONE = Specimen present but no voucher 5.     Source of
 Data = Primary source for data inputed on GenBank  
 **Combined_best_scheme.txt** 1.     Partition and evolutionary model
 suggested by PartitionFinder2 for the combined mitochondrial and nuclear
 dataset. **Mitochondrial_best_scheme.txt** 1.     Partition and
 evolutionary model suggested by PartitionFinder2 for the mitochondrial
 dataset. **Nuclear_best_scheme.txt** 1.     Partition and evolutionary
 model suggested by PartitionFinder2 for the nuclear dataset.
 **Combined_Bayesblock.txt** 1.     Bayesblock of the combined
 mitochondrial and nuclear dataset inputed into Cipres.
 **Calibrated_Bayesblock.txt** 1.     Bayesblock of the time-calibrated
 combined mitochondrial and nuclear dataset inputed into Cipres.
 **Mito_only_Bayesblock.txt** 1.     Bayesblock of the mitochondrial
 dataset inputed into Cipres. **Nuclear_only_Bayesblock.txt** 1.    
 Bayesblock of the nuclear dataset inputed into Cipres. **Combined.nex**
 1.     Nexus file of the combined mitochondrial and nuclear dataset.
 2.     Gene partitions are as follows: * Cytb 1-1143 * COI 1144-2678 * GHR
 2679-3558 * IRBP/RBP3 3559-4824 * BRCA1 4825-5857
 **Mitochondrial_only.nex** 1.     Nexus file of the combined mitochondrial
 and nuclear dataset. 2.     Gene partitions are as follows: * Cytb 1-1143
 * COI 1144-2682 **Nuclear_only.nex** 1.     Nexus file of the combined
 mitochondrial and nuclear dataset. 2.     Gene partitions are as follows:
 * GHR 1-880 * IRBP/RBP3 881-2146 * BRCA1 2155-3175 ## **Sharing/Access
 information** This dataset was made possible by data found on GenBank. See
 individual accession numbers for the specific sequences used from GenBank
 (). ## **Code/Software** All
 code included was computed in R Studio 2022.02.1.