An inbreeding perspective on the effectiveness of wildlife population defragmentation measures: A case study on wild boar (Sus scrofa) of Veluwe, The Netherlands
Data files
Jan 12, 2024 version files 21.13 MB
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de_jong_2024_wild_boar_snp_genotypes.RData
21.13 MB
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README.md
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Abstract
Pervasive inbreeding is a major genetic threat of population fragmentation and can undermine the efficacy of population connectivity measures. Nevertheless, few studies have evaluated whether wildlife crossings can alleviate the frequency and length of genomic autozygous segments. Here, we provided a genomic inbreeding perspective on the potential effectiveness of mammal population defragmentation measures. We applied a SNP-genotyping case study on the ~2500 wild boar Sus scrofa population of Veluwe, The Netherlands, a 1000-km2 Natura 2000 protected area with many fences and roads but also, increasingly, fence openings and wildlife crossings. We combined a 20K genotyping assessment of genetic status and migration rate with a simulation that examined the potential for alleviation of isolation and inbreeding. We found that Veluwe wild boar subpopulations are significantly differentiated (FST-values of 0.02-0.07) and have low levels of gene flow. One noteworthy exception was the Central and Southeastern subpopulation, which were nearly panmictic and appeared to be effectively connected through a highway wildlife overpass. Estimated effective population sizes were at least 85 for the meta-population and ranging from 31 to 52 for the subpopulations. All subpopulations, including the two connected subpopulations, experienced substantial inbreeding, as evidenced through the occurrence of many long homozygous segments. Simulation output indicated that whereas one or few migrants per generation could undo genetic differentiation and boost effective population sizes rapidly, genomic inbreeding was only marginally reduced. The implication is that ostensibly successful connectivity restoration projects may fail to alleviate genomic breeding of fragmented mammal populations. We put forward that defragmentation projects should allow for (i) monitoring of levels of differentiation, migration and genomic inbreeding, (ii) anticipation of the inbreeding status of the meta-population, and, if inbreeding levels are high and/or haplotypes have become fixed, (iii) consideration of enhancing migration and gene flow among meta-populations, possibly through translocation.
Dataset underlying the article: De Jong et al. (2024) An inbreeding perspective on the effectiveness of wildlife population defragmentation measures - a case study on wild boar (Sus scrofa) of Veluwe, The Netherlands
https://doi.org/10.5061/dryad.vmcvdnd0p
The dataset contains individual information and SNP genotypes of wild boar (Sus scrofa) of populations of Northwest Europe, along with the R-script used to analyze the genotype data for the accompanying research article.
Part of these genotypes have been presented earlier. Wild boar samples from the Netherlands are obtained from Goedbloed et al. 2012; wild boar samples from Luxemburg, France and Germany are obtained from De Jong et al. 2023). New in this dataset is the much larger number of samples from Veluwe, The Netherlands, which allowed for examination of the spatial genetic structure and inbreeding status.
Samples were obtained from animals culled within ongoing population management and disease monitoring programs, or from animals that were road traffic victims. No animals were captured or killed for the goal of the purpose of this research.
After DNA extraction, samples were genotyped with the Illumina porcine SNP60 beadchip the Illumina porcine SNP80 beadchip. Consequently, for each individual there is a considerable fraction of loci with missing data; the analyses in the accompying research paper are based on the subset of loci that occur on both beadchips.
Description of the data and file structure
The data is presented as .rds file, which can be loaded in R which the readRDS() function. The file contains three R objects, namely ind,snp and all. all is a genlight object, supported by the R-package adegenet, with SNP-genotypes.
Ind is a dataframe containing information on the individuals in the same order as the individuals presented in the genlight object. SNP is a dataframe containing information on the SNPs, in the same order as the SNPs presented in the genlight object.
Sharing/Access information
Wild boar samples from the Netherlands were obtained from Goedbloed et al. 2012; wild boar samples from Luxemburg, France and Germany are obtained from De Jong et al. 2023).
- Goedbloed, D. J., Megens, H. J., van Hooft, P., Herrero-Medrano, J. M., Lutz, W., Alexandri, P., et al. (2012). Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol. Ecol. 22, 856–866. doi: 10.1111/j.1365-294X.2012.05670.x
- de Jong, J. F., Iacolina, L., Prins, H. H. T., van Hooft, P., Crooijmans, R. P. M. A., van Wieren, S. E., et al. (2023). Spatial genetic structure of European wild boar, with inferences on late-Pleistocene and Holocene demographic history. Heredity 12, 1–10. doi: 10.1038/s41437-022-00587-1
Code/Software
In addition, the repository contains the R-script, which relies on the R-wrapper SambaR (de Jong et al. 2021). With this script, the above mentioned data files can be loaded and analyzed in R, following the same procedure as applied in the accompanying research article.
An explanation of the functionalities of SambaR is provided in: de Jong, M. J., de Jong, J. F., Rus Hoelzel, A., Janke, A., and Menno de Jong, C. J. (2021). SambaR: An R package for fast, easy and reproducible population-genetic analyses of biallelic SNP data sets. Wiley Online Library 21 (4), 1369–1379. doi: 10.1111/1755-0998.13339
Using animals culled within population management programs as well as traffic victimes, samples were collected of wild boar from Veluwe (The Netherlands) and reference populations in Northwestern Europe. DNA was extracted and genotyped using porcine SNP-chips.