Data from: Spatial genetic structure and diversity reveal central-marginal patterns in the rare freshwater mussel, Pleurobema riddellii
Data files
Sep 24, 2026 version files 27.95 MB
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mean_gprob_P_riddellii.csv
27.94 MB
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Pop_rivers.csv
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README.md
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Abstract
This dataset provides genome-wide posterior genotype probability data for the Louisiana Pigtoe, Pleurobema riddellii, a rare freshwater mussel endemic to the Gulf Coastal Plain of the southern USA. The primary data is in the form of a multi-population genotype probability matrix generated from restriction-associated DNA sequencing, comprising 351individuals sampled from across the species range. In this matrix, rows correspond to individual samples and columns correspond to single nucleotide polymorphism loci, with values representing posterior genotype probabilities estimated using the Bayesian clustering program ENTROPY and averaged across models with k=2 through k=5. The dataset includes 12062 loci and is accompanied by a metadata file linking individual sample identifiers to their corresponding river basin designations. Together, these files enable population genomic analyses that account for genotype uncertainty, including the inference of population structure, genetic differentiation, demographic history, and biogeographic comparisons across drainages. All samples were collected under appropriate permits, and no sensitive locality information beyond basin-level assignment is included.
Dataset DOI: 10.5061/dryad.12jm63zc0
Description of the data and file structure
These data were generated as part of a population genomic study of the Louisiana Pigtoe (Pleurobema riddellii) aimed at characterizing genomic variation across the species' geographic range. Tissue samples were collected from individuals sampled across seven river basins within the Gulf Coastal Plain region of the southern United States. Genomic data were generated using restriction-site associated DNA sequencing, producing genome-wide single nucleotide polymorphism data for hundreds of individuals. Bayesian genotype inference was used to estimate posterior genotype probabilities, which were summarized into a matrix for downstream population genomic analyses.
Files and variables
File: Pop_rivers.csv
Description: This file contains metadata linking each sampled individual to its river basin of collection. Rows correspond to individual sample identifiers, and an associated column indicates the river basin from which each individual was collected. Basin designations are provided at the drainage level and are intended to facilitate population-level analyses and grouping of individuals in downstream population genomic analyses.
Variables
- ind: individual 3-letter, 3-number identifier unique to each individual.
- pop: 3-letter population identifier (found within individual identifier as well).
- Basin: River basin of collection.
File: mean_gprob_P_riddellii.csv
Description: Genotype probability matrix containing data for each individual (row) across 12,062 single-nucleotide polymorphism loci (columns). The first several columns contain metadata pertaining to each individual (row).
Variables
- ind: Individual identifier sequence (3 letters and 3 numbers).
- pop: 3-letter locality designator, also found within the individual identifier.
- Basin: River basin of collection
- locus0 - 12,062: Columns representing the estimated genotype probabilities for each locus across all loci.
Code/software
Posterior genotype probabilities were estimated using the Bayesian clustering software ENTROPY (Gompert et al. 2014), which is designed for population genomic inference from genotype likelihood or probability data and explicitly accounts for uncertainty in genotype estimation. ENTROPY was run across multiple clustering models (k=2-5) using genotype likelihoods derived from restriction-site associated DNA sequencing data. For each model, posterior probabilities were estimated for all individuals across all loci. Genotype probabilities were then summarized and averaged across k models (after checking for high convergence across models) using R (version 4.3.0) to generate a single probability matrix for downstream analyses.
Reference:
Gompert, Z., Lucas, L. K., Buerkle, C. A., Forister, M. L., Fordyce, J. a, & Nice, C. C. (2014). Admixture and the organization of genetic diversity in a butterfly species complex revealed through common and rare genetic variants. Molecular Ecology, 23(18), 4555–4573. https://doi.org/10.1111/mec.12811