Data from: The great rift valley is a greater biogeographic barrier than the blue Nile Valley for six Ethiopian highland passerines in the Eastern Afromontane biodiversity hotspot
Data files
Aug 16, 2024 version files 11.12 GB
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FASTQ_Data_Locations.txt
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README.md
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Zosterops_poliogastrus_EB090_R2.fastq.gz
Abstract
The Ethiopian Highlands are divided by lowland biogeographic barriers, including the Blue Nile Valley (BNV) and Great Rift Valley (GRV). We show that the GRV is a more pronounced phylogeographic break than the BNV for six focal passerines. Previous research suggests that the BNV greatly shaped phylogeographic patterns in relatively sedentary montane taxa such as frogs and rodents, whereas the GRV shaped phylogeographic patterns in volant taxa such as birds. However, no previous research simultaneously compares the impact of each valley on phylogeographic patterns in birds, and as these barriers vary in geographic extent and topography, the relative extent of their effects on gene flow is unclear. Using whole genome resequencing, we quantified genetic variation in six montane forest passerines in the Ethiopian Highlands and found that their phylogeographic patterns varied, with general trends distinct from those of taxa that were previously studied across the same barriers. Genetic variation was assessed by estimating genome-wide genetic diversity (HO), demographic history, phylogeographic structure, and phylogeographic concordance among taxa. Population pairs flanking the GRV showed higher FST and more distinct population clusters in PCA than those separated by the BNV. HO was broadly consistent across populations, excluding noticeable reductions in two populations (one population each in two separate species). The overall phylogenetic signature and concordance across study taxa supported populations separated by the BNV as sister and populations southeast of the GRV as most distinct.
README: The Great Rift Valley is a greater biogeographic barrier than the Blue Nile Valley for six Ethiopian Highland passerines in the eastern Afromontane biodiversity hotspot
https://doi.org/10.5061/dryad.z34tmpgp2
Illumina shotgun WGS raw reads in FASTQ format for individual bird blood samples.
Description of the data and file structure
Raw sequencing data were previously uploaded to NCBI and are found at the NCBI links provided in the Sharing/Access information section. Each blood sample has an NCBI SRA number within each BioProject provided in either link (PRJNA948542 and PRJNA605410). Within each SRA, the FASTQ files have a library name that includes the species name and sampling ID of each bird sampled (e.g. "Zosterops_poliogastrus_EB049"). The sampling IDs correspond to those in the paper.
Sharing/Access information
Links to NCBI data locations:
- https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA948542 (Choke Mountains FASTQ files from this project)
- https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA605410 (Previously uploaded Bale and Menagesha FASTQ files from Manthey et al. 2022)
Code/Software
Code was written in R and Shell. Each code file that corresponds to a particular analysis will have that analysis name at the beginning on the file name.
Methods
Blood was sampled from six species for downstream molecular analyses: Cossypha semirufa, Crithagra tristriata, Melaenornis chocolatinus, Sylvia galinieri, Turdus abyssinicus, and Zosterops poliogastrus. Of these, S. galinieri, C. tristriata, M. chocolatinus, and Z. poliogastrus are endemic to high elevations in the Horn of Africa. C. semirufa and T. abyssinicus are found throughout much of the EABH. Permit limitations led to n ≤ 3 individuals captured per species per region. All sampling took place with permissions from the Ethiopian Wildlife Conservation Authority and local landowners. DNA was extracted from blood samples using the QIAGEN (Hilden, Germany) DNeasy Blood and Tissue Kit following manufacturer protocols. DNA samples were sent to the Oklahoma Medical Research Foundation (OMRF) Clinical Genomics Center for Illumina shotgun sequencing on either the HiSeq3500 or NovaSeq6000. Samples were multiplexed on flow cells with libraries from other projects and sequenced with a target of ~10–20× genomic coverage.