DOI: 10.5061/dryad.r4xgxd2q4
title: Data from: Genomic and phenotypic delimitation of species in a temperate aquatic biodiversity hotspot

description: Biologists have relied on morphological characteristics to identify,
 define, and formally describe species for the past 250 years. The advent
 of phylogenetic species concepts and the introduction of molecular data
 have spawned new species delimitation methods applicable to a wide range
 of eukaryotic lineages. However, these approaches heavily emphasize
 genomic data, often overlooking phenotypic traits. We present and
 implement a species delimitation approach that utilizes genome-wide
 markers from ddRAD-seq and meristic morphological traits, which have long
 been used to identify and delineate fish species. Our methodology employs
 unsupervised machine learning to analyze morphological data without a
 priori species assignments, allowing phenotypic patterns to emerge
 independently from genomic-based species delimitation. We apply our
 combined genomic and phenotypic methodology to the freshwater systems of
 Southeastern North America, a biodiversity hotspot where conservation
 efforts are hampered by an incomplete knowledge of species diversity. Our
 investigation focuses on the darter clade Allohistium, a threatened
 lineage comprising two described species. Through phylogenomic, population
 genetic, and phenotypic model comparisons, we provide evidence supporting
 the delimitation of a third species of Allohistium, which we formally
 describe. Our approach shows how unsupervised machine learning can reveal
 cryptic morphological diversity that might otherwise be obscured by
 taxonomic preconceptions. This study demonstrates that model testing using
 diverse lines of evidence yields a more comprehensive, data-driven
 hypothesis of species diversity.
description: # Data from: Genomic and phenotypic delimitation of species in a temperate
 aquatic biodiversity hotspot Dryad
 DOI: https://doi.org/10.5061/dryad.r4xgxd2q4 ## ipyrad.tar iPyrad
 parameters for ddRAD assembly * params-Allohistium_ref.txt: ipyrad
 parameter file used to assemble dataset, no missing data filtering *
 params-Allohistium_ref_m80p.txt: ipyrad parameter file used to assemble
 dataset, no missing data filtering with a min_samples_locus threshold of
 80 samples (&gt;80% samples per locus) * params-Allohistium_ref_m90p.txt:
 ipyrad parameter file used to assemble dataset, no missing data filtering
 with a min_samples_locus threshold of 90 samples (&gt;90% samples per
 locus) * params-Allohistium_ref_m95p.txt: ipyrad parameter file used to
 assemble dataset, no missing data filtering with a min_samples_locus
 threshold of 95 samples (&gt;95% samples per locus) ## IQTree.tar Data,
 scripts, and results of IQGTree analyses * m80p *
 Allohistium_ref_m80p.phy: phylip formatted concatenated ddRAD alignment,
 min 80% samples per locus * runIQTree.sh: script used to run IQTree
 analyses * Allohistium_ref_m80p.phy.*: IQTree results, see IQTree
 documentation for more details * m90p * Allohistium_ref_m90p.phy: phylip
 formatted concatenated ddRAD alignment, min 90% samples per locus *
 runIQTree.sh: script used to run IQTree analyses *
 Allohistium_ref_m90p.phy.*: IQTree results, see IQTree documentation for
 more details * m95p * Allohistium_ref_m95p.phy: phylip formatted
 concatenated ddRAD alignment, min 95% samples per locus * runIQTree.sh:
 script used to run IQTree analyses * Allohistium_ref_m95p.phy.*: IQTree
 results, see IQTree documentation for more details ## BPP.tar Data and
 scripts to run BPP using ddRAD data and calculate GDI values. See
 [https://github.com/dmacguigan/gdiPipeline](https://github.com/dmacguigan/gdiPipeline) for more info. * Allo_GDIPipeline.R: R script to set up BPP runs and calculate GDI * Allo_bpp_loci.txt: alignments of ddRAD loci used for BPP analyses * Allo.tree: species guide tree for nested BPP analyses * Allo.Imap.txt: map of taxa to tips in guide tree ## VCFs.tar Script and data used to filter ddRAD SNPs * VCFToolsPipeline.sh: script used to filter ddRAD SNPs for FEEMS and LEA analyses * Allohistium.vcf: original ddRAD data, outgroups excluded * Allohistium.m95p.unlinked.vcf: filtered ddRAD data ## FEEMS.tar Script and data for FEEMS analysis * Allo_feems.ipynb: script to run FEEMS * Allo.boundary.txt: polygon boundary for FEEMS network * Allo.coord.txt: sample coordinates, matches order of samples in Allohistium.m95p.unlinked.vcf ## LEA_SNMF.tar Script for LEA population structure analysis * ddRAD_LEA.R: R script to run LEA SNMF population structure analysis, uses Allohistium.m95p.unlinked.vcf as input ## PhenoDelimit.tar Scripts and data to run PhenoDelimit analyses * Allohistium * phenoDelimit_Allohistium.R: R script to run phenoDelimit * Allohistium_meristic_specimensIDs.csv: specimen ID data frame, see Table S3 in the Supplementary Materials for additional specimen information * Allohistium_meristic_traits.csv: meristic and squamation trait data, rows match order of Allohistium_meristic_specimensIDs.csv * Allohistium_models.csv: delimitation models for PhenoDelimit, rows match order of Allohistium_meristic_specimensIDs.csv * DarterSisterSpecies: contains 13 subfolders for darter species pairs, all of which contain the following * phenoDelimit.R: script to run PhenoDelimit * specimen_IDs.csv: specimen ID data frame * traits.csv: meristic trait data, rows match order of specimen_IDs.csv * models.csv: delimitation models for PhenoDelimit, rows match order of specimen_IDs.csv Trait abbreviations in Allohistium_meristic_traits.csv and all traits.csv files * LL: number of lateral line scales * PoreLL: number of pored lateral line scales * AbLL: number of scale rows above the lateral line * BlwLL: number of scale rows below the lateral line * Trans: number of transverse scale rows * CD: number of circum-caudal-peduncular scale rows * D1: number of first dorsal fin spines * D2: number of second dorsal fin rays * A1: number of anal fin spines * A2: number of anal fin rays * P1: number of pectoral fin rays * Nape: percentage of nape squamation * Cheek: percentage of cheek squamation * Opercle: percentage of opercular squamation * Breast: percentage of breast squamation * Belly: percentage of belly squamation