DOI: 10.5061/dryad.vmcvdnd0p
title: An inbreeding perspective on the effectiveness of wildlife population defragmentation measures: A case study on wild boar (Sus scrofa) of Veluwe, The Netherlands

description: 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.
description: 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.
description: # SNP genotypes of wild boar of Veluwe (Sus scrofa) and reference
 populations 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 
 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