DOI: 10.5061/dryad.3ffbg79xg
title: Peripheral budding following range expansion explains diversity and distribution of one‐sided Livebearing fish

description: Peripheral budding occurs when populations diverge from a widespread
 parental population and speciate along its periphery, facilitated by the
 interaction of ecological and geographic barriers. This phenomenon results
 in species that contrast in range size and ecological tolerance and can
 lead to confounding phylogenies. Here we examine patterns of peripheral
 budding in the Jenynsia lineata species complex using a genomic approach
 via RAD sequences. The J. lineata species complex is a group of
 live‐bearing fish in South America that shows signals of peripheral
 budding through asymmetric range sizes, J. lineata being widespread, and
 with a confounding and unresolved phylogeny. Our goal was to adequately
 classify the J. lineata species complex, delimit species within the
 complex via multiple approaches, and identify signals of introgression to
 better understand the underlying evolutionary patterns. We collected 85
 samples from the species complex for DNA extraction and performed RAD
 sequencing to generate genome‐wide molecular markers for phylogenetic
 analyses. We found evidence of six distinct genetic groups within the
 complex and delimited at least five species, with a new species of
 Jenynsia in Northern Argentina along the periphery of J. lineata. Jenynsia
 lineata was recovered as the most recently diverged species in our
 phylogeny. This placement, along with observed patterns of introgression
 between species, suggests peripheral budding to have facilitated
 speciation in the J. lineata species complex, following a range expansion
 of a parental J. lineata. Our results show genomic patterns associated
 with peripheral budding and support the utility of using peripheral
 budding to better understand confounding phylogenetic patterns.
description: # Peripheral budding following range expansion explains diversity and
 distribution of one‐sided livebearing fish Dataset DOI:
 [10.5061/dryad.3ffbg79xg](10.5061/dryad.3ffbg79xg) ## Description of the
 data and file structure Caudal fin tissues were collected from one-sided
 live bearer fish (*Jenynsia sp.*) in Argentina, Uruguay, and Brazil
 between 2013 and 2017. Tissues were preserved in 100% ethanol. DNA was
 extracted via a double-digest restriction-associated DNA (ddRAD-seq)
 protocol. All raw genomic sequences (fastq files) are available  in the
 NCBI sequence read archive (SRA) (BioProject: PRJNA822935 [Accession:
 SRX14732231–SRX14732245; SRX14732201–SRX14732206; SRX14732208–SRX14732216;
 SRX14732189–SRX14732191] and BioProject: PRJNA1238845 [Accession:
 SAMN47485203–SAMN47485265]). An 83 individuals dataset and 94 individuals
 dataset was used in these analyses. The 83 individuals dataset includes
 sample sequences from all species of the *Jenynsia lineata* species
 complex (*onca, luxata, lineata,* and *darwini).* The 94 individuals
 dataset includes all sequences from the 83 individuals dataset with the
 addition of sequences from a genomic outgroup, *J. obscura.* ### Files and
 variables #### File: data.zip **Description:** The data folder contains
 data files used for *.R scripts. This folder also contains raw data about
 the sampling and output probabilities from the Delineate analysis. Files:
 `{species/population}.thetas.idx.pestPG` Description: Output files from
 `diversity.sh`. Describes several diversity metrics for each
 population/species. Files used in `Jenynsia_genetic_diversity.R` script.
 *Variables*:  * (indexStart,indexStop): start and stop location in the
 index * (firstPos_withData,lastPos_withData): first and last position with
 data within the window * (WinStart,WinStop): start and stop location in
 the chromosome * Chr: contig for the window * WinCenter: center position
 of the window * tW: Watterson's θ * tP: pairwise θ * tF: Fu and Li’s
 θF * tH: Fay and Wu’s θH * tL: Fu and Li’s θL * Tajima: Tajima's D
 estimation * fuf: Fu and Li’s D * fud: Fu and Li’s F * fayh: Fay and Wu’s
 H * zeng: Zeng's E * nSites: number of SNPs in that window File:
 `jenynsia_data_mapping.csv` Description: Information about where samples
 were collected. Used in `Jenynsia_map.R`. *Variables*: * order: row id
 number * id: unique id for each sample * full_id: unique id number along
 with population information letters * population: assigned
 population/species * lat: latitude in WGS84 projection of the sampled body
 of water * long: longitude in WGS84 projection of the sampled body of
 water * population_map: population identifier used for plotting sampling
 locations Files: `plink.{#}.Q.csv` Description: Output from
 `Jenynsia_admixture.sh`. Ancestry proportions of each k (#) cluster for
 each individual. `popmap.txt` gives the order of the individuals, 1-83.
 *Variables*: * Each column represents a different cluster, 1:k(#) * Each
 row is a different individual File: `popmap.txt` Description: Population
 map of the collected samples *Variables*: * Column 1: sample ID * Column
 2: population identifier File:
 `Delineate_tree_probability_output.json.zip` Description: This file is
 output from `delineate.sh` from the scripts folder. The file describes the
 different tree probabilities from the delineate analysis. #### File:
 inputs.zip **Description:** The inputs folder contains inputs for genomic
 analyses. Phylip and nexus files are also included from phylogenetic
 analyses. File: `bfd_{test}.xml` Description: These files are used as
 inputs in the `BFD.sh` analyses. File: `{species/population}.txt`
 Description: These files contain a list of samples for each
 species/population. If the file is popmap#, that is the list of species
 used in the analyses that included an outgroup (94) or not (83). File:
 `delin_guide_tree_final.nex` Description: This file is the guide tree used
 in the `delineate.sh` analysis. It is in nexus format. File:
 `delin_input_final.tsv` Description: This file is the metadata used in the
 `delineate.sh` analysis. *Variables*: * lineage: These are the different
 population groupings for the SNAPP analysis * species: The species being
 tested for the several population lineages * status: 1 is that this
 species has previously been recognized as distinct. 0 is that there is
 currently no strong evidence if this is a distinct species or not. File:
 `dsuite.fb.txt` Description: This file is an output file describing the
 branches from the `dsuite.sh` analysis. File: `{dataset}.nexus`
 Description: These files are in standard nexus format. Files with the
 number 30, 83, or 94 correspond to the 30 individuals, 83 individuals, and
 94 individuals datasets. `svd.nexus` was used as input to the SVDquartets
 analysis. File: `{dataset}.phy` Description: These files are in standard
 phylip format. Files with the number 83 or 94 correspond to the 83
 individuals and 94 individuals datasets. File: `snapp_remove.txt`
 Description: This is a list of individuals that were not included in the
 30 individuals dataset analyses. File: `snapp.xml` Description: This file
 is the input file for the `scripts/SNAPP.sh` analysis. File:
 `svd.newick.txt` Description: This file is the input file for the
 SVDquartets analysis. The file contains a tree in newick format of the
 Jenynsia species. File: `taxapartitions.txt` Description: This file is the
 input file for the SVDquartets analysis. The file contains set information
 for each species that corresponds to the order of species in the
 `svd.newick.txt` file. #### File: scripts.zip **Description:** The scripts
 folder contains the scripts used in analyses. The code in the *.sh files
 was written for use on an HPC sungrid engine system. ## Code/software
 **Genomic analyses** **dDocent** This code was run in a conda environemnt
 \ dDocent v2.9.4 needs to be installed
 [https://ddocent.com/bioconda/](https://ddocent.com/bioconda/)\ fastq
 files must be in the working directory (see SRA accession information
 above)\ For the 83 and 30 individuals data set do not have *J. obscura*
 samples in the working directory \ For the 94 individuals dataset, include
 all fatsq files \ Run \ `scripts/ddocent.sh`\ Rename output to
 TotalRawSNPs_83.vcf and ddocent_Final_83.recode.vcf after running on 83
 individuals \ Rename output to TotalRawSNPs_94.vcf and
 ddocent_Final_94.recode.vcf after running on 94 individuals **Filtering
 VCF files** vcftools and vcffilter need to be installed in conda \ run
 `scripts/Jenynsia_filter.sh` **File conversion** download vcf2phylip
 [https://github.com/edgardomortiz/vcf2phylip](https://github.com/edgardomortiz/vcf2phylip) to convert to nexus, fasta, and phylip formats\ run \ for 83 individuals dataset \ `python vcf2phylip.py -i inputsfiltered_final_83_LD_pruned.recode.vcf -f -n -b`\ for 30 individuals dataset \ `python vcf2phylip.py -i inputs/filtered_final_30_nomiss_LD_pruned_sub1000.vcf -f -n -b`\ for 94 individuals dataset \ `python vcf2phylip.py -i inputs/filtered_final_94_LD_pruned.recode.vcf -f -n -b` **PCA** ipyrad needs to be installed in conda \ compress vcf file \ `bgzip inputs/filtered_final_83.recode.vcf &gt; inputs/filtered_final_83.recode.vcf.gz`\ index the compressed file \ `tabix inputs/filtered_final_83.recode.vcf.gz`\ then run the python file `ipyrad_converter_pca`\ followed by `ipyrad_pca.py`\ A PCA figure will be output **Admixture** ADMIXTURE v 1.3.0\ PLINK needs to be installed in conda \ run `scripts/Jenynsia_admixture.sh`\ Outputs CV errors and files for plotting each K in r \ Use `admixture_plot.R` to plot figure in R **IQtree and RAXML** IQtree 2.4.0 and RAXML v8 need to be installed in conda\ Run\ `iqtree.sh`\ Run\ `raxml.sh` **SVDQuartets** generate file for SVDQuartets \ ` cat inputs/filtered_final_83_LD_pruned.recode.min4.nexus inputs/taxpartitions.txt &gt; inputs/svd.nexus`\ PAUP v.4.0.a168 needs to be installed and launched \ Run \ `exe inputs/svd.nexus`\ Then run \ `svdq taxpartition=fish showScores=no seed=1234568 bootstrap nreps=1000 treeFile=svd.tre;` To save the consenses tree in newick format run \ `savetree` **SNAPP** Beast2 v2.7.7 needs to be downloaded \ Run 2 times\ `scripts/SNAPP.sh` **BFD*** Beast2 v2.6.7 needs to be downloaded \ Run\ `scripts/BFD.sh` **DELINEATE** DELINEATE v1.2.3 needs to be downloaded to conda\ Guide tree is generated from the two SNAPP runs consensus tree using the TreeAnnotator application\ Run `scripts/delineate.sh`\ Output of tree probabilty analysis can be found in the data folder under `Delineate_tree_probabilty_output.json` **TreeMix** compress vcf file \ `bgzip inputs/filtered_final_94.recode.vcf &gt; inputs/filtered_final_94.recode.vcf.gz`\ index the compressed file \ `tabix inputs/filtered_final_94.recode.vcf.gz`\ Run python file `ipyrad_converter_treemix.py`\ Followed by `ipyrad_treemix.py` Save output in folder named "tree" within the data folder for further analysis in R\ To plot use python file `ipyrad_treemix_plot.py` **Dsuite** Download dsuite v0.5.r53 [https://github.com/millanek/Dsuite](https://github.com/millanek/Dsuite)\ Uses `svd.newick.txt` output from SVDquartets analysis\ Run `scripts/dsuite.sh` Outputs `dsuite.fb.txt` \ To plot use `dtools.py`, which is part of the dsuite software\ run\ `python3 dtools.py dsuite.fb.txt inputs/svd.newick.txt` **Nucleotide Diversity** ANGSD needs to be downloaded in conda \ Run \ `diversity.sh` Plotting is done in R using `Jenynsia_genetic_diversity.R` ## Access information Other publicly accessible locations of the data: * [https://github.com/TD-Lab-NotreDame/Peripheral_budding_Jenynsia_lineata_complex](https://github.com/TD-Lab-NotreDame/Peripheral_budding_Jenynsia_lineata_complex) * All raw genomic sequences (fastq files) are available  in the NCBI sequence read archive (SRA) (BioProject: PRJNA822935 [Accession: SRX14732231–SRX14732245; SRX14732201–SRX14732206; SRX14732208–SRX14732216; SRX14732189–SRX14732191] and BioProject: PRJNA1238845 [Accession: SAMN47485203–SAMN47485265]).