Data from: Population genomics of a rare and a common wood-inhabiting fungal species across Europe
Data files
Mar 05, 2026 version files 545.17 MB
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Ac_filtered_individual_MM90percent_SNPs_only.vcf.gz
259.78 MB
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Ac_IBS.mdist
106.81 KB
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Ac_IBS.mdist.id
5.99 KB
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Ac_region_genotypeMatrix99.traw
716.79 KB
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env.rds
19.54 KB
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Fp_filtered_individual_MM90percent_SNPs_only.vcf.gz
252.81 MB
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Fp_IBS.mdist
13.28 KB
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Fp_IBS.mdist.id
2.12 KB
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Fp_region_genotypeMatrix99.traw
31.67 MB
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README.md
4.42 KB
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Supporting_information_5_Figures.R
36.45 KB
Abstract
Many species have become threatened during the Anthropocene, requiring conservation strategies based on biological evidence. Wood-inhabiting fungi face multiple threats due to a complex interplay of a short lifespan, removal of dead wood as a resource, and climate change. Furthermore, rare fruiting events might restrict dispersal via spores, leading to significant population genetic structure. Yet, little is known about the genetic structure of both rare and common wood-inhabiting fungal species across Europe. Here, we investigate the rare polypore fungus Antrodiella citrinella, which co-occurs with the common wood-decay fungus Fomitopsis pinicola. We analyzed a total of 149 individuals of both species across 13 countries, sequenced their genomes, and analyzed single-nucleotide polymorphisms. Based on a broad set of analyses, we found a very weak population structure in A. citrinella, suggesting historically wide dispersal and effective gene flow across Europe. In contrast, we found support for two moderately differentiated populations following a southwest-northeast separation in F. pinicola, possibly due to dispersal limitation through its relatively larger spores, a more intense forest use history in southern Europe, and a post-glacial history of co-immigration with the main host tree species, Norway spruce. While the weak to moderate genetic structure of wood-inhabiting fungi suggests historically sufficient habitat connectivity, conservation measures should consider strategies providing deadwood as an important habitat to restore and maintain connectivity throughout Europe.
Dataset DOI: 10.5061/dryad.dr7sqvbcd
Description of the data and file structure
The data cointain 5 files:
- Supporting information_5_Figures.R
- Ac_filtered_individual_MM90percent_SNPs_only.vcf.gz (VCF file for Antrodiella citrinella)
- Fp_filtered_individual_MM90percent_SNPs_only.vcf.gz (VCF file for Fomitopsis pinicola)
- Ac_region_genotypeMatrix99.traw (TRAW file for Antrodiella citrinella for PCA)
- Fp_region_genotypeMatrix99.traw (TRAW file for Fomitopsis pinicola for PCA)
- Ac_IBS.mdist and Ac_IBS.mdist.id
- Fp_IBS.mdist and Fp_IBS.mdist.id
- env.rds
Files and variables
File: Supporting_information_5_Figures.R
Description: R code for the main figures of the manuscript (main body)
This file contains code on how the following files can be loaded and analysed:
File: Ac_filtered_individual_MM99percent_SNPs_only.vcf.gz
Description: VCF file for Antrodiella citrinella for ADMIXTURE analysis
File: Ac_filtered_individual_MM90percent_SNPs_only.vcf.gz
Description: VCF file for Fomitopsis pinicola for ADMIXTURE analysis
File: Fp_filtered_individual_MM99percent_SNPs_only.vcf.gz
Description: TRAW file for Antrodiella citrinella for PCA
File: Ac_IBS.mdist and Ac_IBS.mdist.id
Description: Data for identity by state (IBS) analysis for Antrodiella citrinella.
File: Fp_IBS.mdist and Fp_IBS.mdist.id
Description: Data for identity by state (IBS) analysis for Fomitopsis pinicola.
Note on how to open .mdist and mdist.id:
In R: read.table
File: env.rds
Description: RDS file with environmental data and coordinates for the spatial distance matrix.
The env.rds contains the following columns:
Here's a description of each variable in your env dataframe:
accession— NCBI BioSample accession numbermessage— API/database query status for sample loadingsample_name— Short sample code (country prefix + site + species abbreviation)organism— Fungal species nameisolate— Type of biological material (fruit body)collection_date— Field collection dateenv_broad_scale— Broad environmental classification (ENVO ontology term)env_medium— Environmental medium (ENVO ontology term)geo_loc_name— Verbatim locality description (country, region, site)lat_lon— Raw lat/long string as submitted to NCBIbiotic_relationship— Nature of biotic interaction (largely empty here)host— Host tree speciessource_material_id— Herbarium/culture collection voucher IDcollector— Name(s) of field collector(s)
country— ISO country codeSample— Cleaned sample identifier (matchessample_name)N_Variants— Total number of genomic variants called per sampleN_SNPs— Number of SNPs (subset of variants)Mean_GQ— Mean genotype quality score across lociMean_DP— Mean sequencing depth across locilat/long— Parsed decimal-degree coordinatescount— Sequential sample indexID— Ordered numeric sample ID
bio_1— Mean annual temperature (°C × 10 or scaled)bio_2— Mean diurnal temperature rangebio_3— Isothermality (bio_2/bio_7 × 100)bio_4— Temperature seasonality (CV)bio_5— Max temperature of warmest monthbio_6— Min temperature of coldest monthbio_7— Temperature annual range (bio_5 − bio_6)bio_8— Mean temperature of wettest quarterbio_9— Mean temperature of driest quarterbio_10— Mean temperature of warmest quarterbio_11— Mean temperature of coldest quarterbio_12— Annual precipitation (mm)bio_13— Precipitation of wettest monthbio_14— Precipitation of driest monthbio_15— Precipitation seasonality (CV)bio_16— Precipitation of wettest quarterbio_17— Precipitation of driest quarterbio_18— Precipitation of warmest quarterbio_19— Precipitation of coldest quarter
Code/software
R version 4.4.0
Packages:
adegenet 2.1.11
vcfR 1.15.0
poppr 2.9.6
tidyverse 2.0.0
openxlsx 4.2.8
terra 1.8.54
geodata 0.6-2
sf 1.0-21
Access information
Other publicly accessible locations of the data:
- NCBI SRA at BioProject-ID PRJNA1222407
