Data from: Testing assertions of widespread introgressive hybridization in a clade of neotropical toads with low mate selectivity (Rhinella granulosa species group)
Data files
Feb 27, 2026 version files 1.05 MB
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dryad_data.zip
1.05 MB
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README.md
4.25 KB
Abstract
Discordance between different genomic regions, often identified through multi-locus sequencing of selected markers, presents particular difficulties in identifying historical processes which drive species diversity and boundaries. Mechanisms causing discordance, such as incomplete lineage sorting or introgression due to interspecific hybridization, are better identified based on population-level genomic datasets. In the toads of the Rhinella granulosa species group, patterns of mito-nuclear discordance and potential hybridization have been reported by several studies. However, these patterns were proposed based on few loci, such that alternative mechanisms behind gene-tree heterogeneity cannot be ruled out. Using genome-wide ddRADseq loci from a subset of species within this clade, we found only partial concordance between currently recognized species-level taxon boundaries and patterns of genetic structure. While most taxa within the R. granulosa group correspond to clades, genetic clustering analyses sometimes grouped distinct taxonomic units into a single cluster. Moreover, levels of admixture between inferred clusters were limited and restricted to a single taxon pair which is best explained by incomplete lineage sorting as opposed to introgressive hybridization, according to D-statistics results. These findings contradict previous assertions of widespread cryptic diversity and gene flow within the R. granulosa clade. Lastly, our analyses suggest that diversification events within the Rhinella granulosa group mostly dated back to the early Pliocene, being generally younger than species divergences in other closely related clades that present high levels of cross-species gene flow. This finding uniquely contradicts common assertions that this young clade of toads exhibits interspecific hybridization.
https://doi.org/10.5061/dryad.p2ngf1vtc
Contents for dryad_data.zip
IMPORTANT: Active Code Repository on GitHub and Zenodo: https://doi.org/10.5281/zenodo.18683244
All analysis code, data, and documentation are maintained at:
https://github.com/daniellerivera/Rhinella_granulosa
The GitHub repository is the primary, actively maintained source for reproducibility and contains:
- All analysis scripts with detailed README files
- Software dependencies and installation instructions
- Input and output files
- Version control for code updates
- Issue tracking for user support
We strongly recommend using the GitHub repository rather than the static files archived here.
Article Citation
Rivera D, Prates I, Caldwell JP, Rodrigues MT, Fujita MK. 2022. Testing assertions of widespread introgressive hybridization in a clade of neotropical toads with low mate selectivity (Rhinella granulosa species group). Heredity 130: 14-21.
DOI: https://doi.org/10.1038/s41437-022-00571-9
File Descriptions
The input files for this study can be found on Dryad. Please see Zenodo for the scripts and other files needed: https://doi.org/10.5281/zenodo.18683244
Admixture/
Population structure analysis using ADMIXTURE.
rgran_ref_m65_randomSNP.str— Genotype data in STRUCTURE format.
DAPC/
Discriminant Analysis of Principal Components performed in the R package adegenet.
rgran_ref_m65_randomSNP.str— Same STRUCTURE-format genotype file as used in Admixture/
D-Suite/
Tests of introgression using D-statistics (ABBA-BABA) implemented in D-Suite.
rgran_ref_1SNP-locus.vcf— VCF file containing one SNP per locus.sets.txt— Tab-delimited file assigning individuals to populations/taxa for D-Suite analysis.plot_order.txt— Order of populations for output plotting.tree.nwk— Newick-format guide tree specifying population relationships and outgroup for D-Suite.
G-PhoCS/
Divergence time and migration rate estimation using G-PhoCS.
gran_ingrps_sub_gphocs.gphocs— Sequence data file in G-PhoCS format.gramir_mercenhum_maj_nomig.ctl— G-PhoCS control file specifying model parameters, priors, and MCMC settings for the no-migration model.
SVDQuartets/
Coalescent-based species tree estimation using SVDQuartets as implemented in PAUP*.
rgran.nex— NEXUS file containing SNP data and PAUP* block with SVDQuartets analysis settings.
TESS3r/
Spatial ancestry and population structure estimation using TESS3r.
rgran_data.geno— Genotype matrix in .geno format.rgran_data.lfmm— Genotype matrix in LFMM format for the same individuals and SNPs.
Software Requirements
- ADMIXTURE
- D-Suite
- R (with packages: adegenet, tess3r, and others listed in individual scripts)
- G-PhoCS
- IQ-TREE
- MrBayes
- PAUP*
- Python (for ADMIXTURE processing)
See GitHub repository for detailed installation instructions and dependencies.
Usage
For detailed usage instructions, see the README.md file in the GitHub repository at https://github.com/daniellerivera/Rhinella_granulosa
Each analysis folder contains input files and scripts. The GitHub repository provides step-by-step instructions for reproducing all analyses.
Support
For questions or issues:
- Open an issue on GitHub: https://github.com/daniellerivera/Rhinella_granulosa/issues
- Contact: Danielle Rivera
Note on Reproducibility
This Dryad entry serves as a permanent DOI for citation purposes. The GitHub repository at https://github.com/daniellerivera/Rhinella_granulosa is the definitive source for code and includes version control, making it superior for reproducibility.
Genomic DNA was extracted, amphified, and sequenced using 16S-specific primers, and used to generate maximum likelihood and bayesian phylogenies using IQTREE and MrBayes. All other data files were generated with double-digest restriction site-associated DNA sequence (ddRADseq) data. Briefly, ddRADseq libraries were generated using SbfI and MspI restriction enzymes, tagging fragments with individual barcodes at the University of Texas at Arlington. Libraries were then sequenced on a single lane on an Illumina HiSeq 2500 platform. The resulting data was demultiplexed using ipyrad, which generated input files for the programs used in this analysis.
