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Population Genetic Structure of Wild Boar and Dispersal Performance Based on Kinship Analysis in the Northern Region of South Korea

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Population Genetic Structure of Wild Boar and Dispersal Performance Based on Kinship Analysis in the Northern Region of South Korea Population Genetic Structure of Wild Boar And Dispersal Performance Based On Kinship Analysis In The Northern Region of South Korea Seung Woo Han ( [email protected] ) Seoul National University College of Veterinary Medicine https://orcid.org/0000-0002-7148-4087 Han Chan Park Yeongnam Daehakgyo: Yeungnam University Jee Hyun Kim Seoul National University College of Veterinary Medicine Jae Hwa Suh NIBR: National Institute of Biological Resources Hang Lee Seoul National University College of Veterinary Medicine Mi Sook Min Seoul National University College of Veterinary Medicine Research Article Keywords: Wild boar, Microsatellites, Population Genetics, Dispersal, Kinship Analysis, Conservation Genetics Posted Date: May 7th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-368091/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/16 Abstract Wild boar (Sus scrofa) is one of the most challenging mammalian species to manage in the wild because of its high reproductive rate, population density, and lack of predators in much of its range. A recent outbreak of African swine fever (ASF) and the transmission into domestic pigs in commercial farms empower the necessity of establishing management strategies of the wild boar population in the northern region of South Korea. A population genetic study, including the dispersal distance estimation of wild boars, is required to prepare ne-scale population management strategies in the region. In this study, both population structure analysis and dispersal distance estimation based on kinship were conducted using 13 microsatellite markers. The results revealed a high level of genetic diversity compared to a previous study. The population was not structured obviously, but there was a slight level of genetic differentiation between groups mainly formed by isolation by distance rather than mountain ridges. The dispersal distance estimation of Korean wild boars based on kinship analysis showed a philopatric pattern in females. However, extensive dispersal ability in both sexes was observed with a considerable proportion. The population genetic status and dispersal traits of wild boars may provide valuable data for planning detailed ASF and wild boar population management strategies in South Korea. Introduction Wild boar (Sus scrofa Linnaeus, 1758) is one of the most extensively distributed, large-sized mammalian species worldwide. Being highly viable and having a high reproductive rate, wild boar spreads its range across most European and Asian regions and the northern part of Africa as native species. Besides, there are introduced populations thriving in the Americas and Australia (Massei and Genov 2004). African swine fever (ASF) is a highly fatal disease for species within the genus Sus, including wild boars and domestic pigs. After the initial outbreak in Georgia in 2007 (Jo and Gortázar 2020), the ASF virus (ASFV) spread quickly all over Eurasia. The virus spread in western European countries in 2014, followed by eastward spread to China in 2018. The rst detection of ASFV was near the Demilitarized Zone (DMZ) in the northern part of South Korea in 2019, spreading toward South Korea’s southern region. As wild boars can share ASFV with domestic pigs, the spread of disease in wild boars may cause massive economic damage in the swine industry (Cadenas-Fernández et al. 2019). Therefore, basic information on the species and population biology of wild boars is required to develop a control strategy against ASF in South Korea. A previous study demonstrated that the South Korean wild boar population showed discrete clustering from China, Russia, Indonesia, and Japan, asserting Taebaek Mountain ridges as a gene ow barrier within the South Korean populations (Choi et al. 2014). However, the relatively small number of South Korean samples used in the study is a limiting factor in interpreting the results. Studies that have described the ecological traits of wild boars in South Korea using ecological methods have been published (Park and Lee 2003; Choi et al. 2006; Lee 2013; Kim et al. 2019). Although several studies about the dispersal ability of wild boars have been conducted in other countries, no such Page 2/16 information is available in South Korea. A study on the home range size of wild boars in South Korea reported four individuals inhabiting 5.13 km2 on average (Choi et al. 2006). However, this research was conducted with a small sample size using the radio-tracking technique within a limited study area and time frame. Although a recent study about habitat preference demonstrated that wild boars prefer ridge mountain environments with substantial food resources (Kim et al. 2019), further studies must establish detailed control strategies for diseases mediated by wild boars in South Korea. Because the spread of infectious diseases in wild animals is closely linked to the dispersal ability of the animals and the gene ow pattern within the frame of the population structure, information on the dispersal distance and pattern and the genetic structure of wild boars has the potential to contribute to the effective disease control strategy. Microsatellite marker is a convenient tool for population and ecological genetic studies. These polymorphic markers, which can estimate the recent history of genetic differentiation at the population level, are widely used in numerous studies to demonstrate the population structure of wild animals in South Korea (Lee et al. 2011; Jo et al. 2017; Hong et al. 2018; Lee et al. 2019). Moreover, a set of microsatellite markers was developed for swine biodiversity applications (Committee 2004). The usefulness of these microsatellite markers has been veried by numerous population genetic studies of wild boars (Costa et al. 2012; Choi et al. 2014; Delgado-Acevedo et al. 2021). Dispersal distance estimation with kinship analysis using polymorphic markers has been reported previously for several species (Cayuela et al. 2018). With a sucient number of samples, this genetic method enables estimating dispersal distance. This study investigated the genetic diversity and population structure of the wild boar population in the northern part of South Korea (Gyeonggi and Gangwon provinces) and estimated the dispersal distance based on kinship analysis of 474 wild boar carcass tissue samples using 13 microsatellite markers. Materials And Methods Sample collection Muscle tissue samples from 474 wild boar carcasses, hunted in Gyeonggi and Gangwon provinces by licensed local hunters from November 2019 until May 2020, were collected by NIBR (National Institute of Biological Resources) for DNA extraction. All the hunting activities were done with a permit from local governments and no specic approval was required for the wild boar carcass sampling in this study. For both genetic structure and distance distribution analyses, samples were divided into 6 geographical groups with similar area sizes (Fig. 1). For the Isolation by Distance (IBD) analysis, 474 samples were reassigned into 24 geographical groups, each group with a minimum of 15 samples in a city size area (Table S1, Fig. S1). PCR amplication and genotyping Page 3/16 DNA was extracted using a QuickGene DNA tissue kit (Fujilm, Tokyo, Japan). Total 13 microsatellite markers developed for swine biodiversity research (Committee 2004) were used. The uorescent-dye- labeled markers were amplied using a multiplex PCR kit (QIAGEN). PCR was conducted with touchdown method under the following conditions: initial denaturation for 15 min at 95°C, followed by 7 touchdown cycles starting from 94°C for 30 s, 67°C for 90 s, and 72°C for 60 s, with annealing temperature decreasing by 2°C per cycle to 53°C. The additional 25 cycles were performed at 94°C for 30 s, 53°C for 90 s, 72°C for 60 s, and a nal extension step at 60°C for 30 min. PCR products were loaded onto a DNA Sequencer (ABI Prism 3730 XL DNA Analyzer, Applied Biosystems) for genotyping. Allele size was determined using GeneMapper v.3.7 (Chatterji and Pachter 2006). For sexing, newly developed primers for wild boars that amplify intron 7 anking regions of ZFX and ZFY genes (Han et al. 2007) were used. The PCR condition for sexing was the same as in the referenced paper. The PCR products were loaded onto EtBr agarose gel to distinguish double bands for males from a single band for females by agarose gel electrophoresis. Data analysis Genetic structure analysis Genotype data from 474 wild boars were integrated with GenAlex v.6.503 (Peakall and Smouse 2006) for obtaining the population genetics parameters such as allele frequencies, expected heterozygosity, and observed heterozygosity under Hardy–Weinberg assumptions. Hardy–Weinberg Equilibrium (HWE) test and null allele test were conducted via GENEPOP v.4.7 (Raymond 1995). The sequential Bonferroni correction was applied to take account of statistical errors for multiple tests (Rice 1989). Genetic structure was estimated with STRUCTURE v.2.3.4 (Pritchard et al. 2000), and an optimal K value was estimated with STRUCTURE HARVESTER using the Evanno method (Evanno et al. 2005; Earl 2012). We used the initial burn-in period of 100,000, followed by 200,000 MCMC (Markov Chain Monte Carlo) iterations with 10 iterations per K. We calculated pairwise FST values between each of the 6 preliminary estimated groups with FSTAT v.2.9.4 (Goudet 1995) based on 5,000 permutations. Based on the pairwise FST values, Nm values were calculated with the equation of Nm=1/4{(1 - FST)/FST}, as an indirect index of a gene ow. Additionally, Slatkin’s linearized pairwise FST values were calculated with 10,000 permutations between 24 groups assigned for IBD analysis using Arlequin v.3.5.2.2 (Excoer et al. 2005). We conducted a Mantel test with 999 permutations using GenAlex v.6.503 to estimate IBD to establish the relationship between genetic and geographic distance matrices. Dispersal distance analysis The kinship relationship between individuals was estimated with ML-RELATE software (Kalinowski et al.
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