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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 0.02-0.13) 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 ranged 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 inbreeding 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

Permeable phylogeographic barriers characterize the vast open ocean, boosting gene flow and counteracting population differentiation and speciation of widely distributed and migratory species. However, many widely distributed species consists of distinct populations throughout their distribution, evidencing that our understanding of how the marine environment triggers population and species divergence are insufficient. The sailfish is a circumtropical and highly migratory billfish that inhabits warm and productive areas. Despite its ecological and socioeconomic importance as a predator and fishery resource, the species is threatened by overfishing, requiring innovative approaches to improve their management and conservation status. Thus, we presented a novel high-quality reference genome for the species and applied a seascape genomics approach to understand how marine environmental features may promote local adaptation and how it affects gene flow between populations. We delimit two populations between the Atlantic and Indo-Western Pacific oceans and detect outlier loci correlated with sea surface temperature, salinity, oxygen, and chlorophyll concentrations. However, the most significant explanatory factor that explains the differences between populations was isolation by distance. Despite recent population drops, the sailfish populations are not inbred. For billfishes in general, genome-wide heterozygosity was found to be relatively low compared to other marine fishes, evidencing the need to counteract overfishing effects. In addition, in a climate change scenario, management agencies must implement state-of-the-art sequencing methods, consider our findings in their management plans, and monitor genome-wide heterozygosity over time to improve sustainable fisheries and the long-term viability of its populations.

SNP data sets can be used to infer a wealth of information about natural populations, including information about their structure, genetic diversity, and the presence of loci under selection. However, SNP data analysis can be a time-consuming and challenging process, not in the least because at present many different software packages are needed to execute and depict the wide variety of mainstream population-genetic analyses. Here, we present SambaR, an integrative and user-friendly R package which automates and simplifies quality control and population-genetic analyses of biallelic SNP data sets. SambaR allows users to perform mainstream population-genetic analyses and to generate a wide variety of ready to publish graphs with a minimum number of commands (less than 10). These wrapper commands call functions of existing packages (including adegenet, ape, LEA, poppr, pcadapt and StAMPP) as well as new tools uniquely implemented in SambaR. We tested SambaR on online available SNP data sets and found that SambaR can process data sets of over 100,000 SNPs and hundreds of individuals within hours, given sufficient computing power. Newly developed tools implemented in SambaR facilitate optimization of filter settings, objective interpretation of ordination analyses, enhance comparability of diversity estimates from reduced representation library SNP data sets, and generate reduced SNP panels and structure-like plots with Bayesian population assignment probabilities. SambaR facilitates rapid population genetic analyses on biallelic SNP data sets by removing three major time sinks: file handling, software learning, and data plotting. In addition, SambaR provides a convenient platform for SNP data storage and management, as well as several new utilities, including guidance in setting appropriate data filters. The SambaR source script, manual and example data set are distributed through GitHub: https://github.com/mennodejong1986/SambaR.