关键词: Sus scrofa SNP feral pig invasive species neural networks translocations wild pig

Mesh : Animals United States Introduced Species Polymorphism, Single Nucleotide Neural Networks, Computer Swine / genetics Sus scrofa / genetics Genotype Ecosystem Animals, Wild / genetics Genetics, Population

来  源:   DOI:10.1111/mec.17489

Abstract:
Globalization has led to the frequent movement of species out of their native habitat. Some of these species become highly invasive and capable of profoundly altering invaded ecosystems. Feral swine (Sus scrofa × domesticus) are recognized as being among the most destructive invasive species, with populations established on all continents except Antarctica. Within the United States (US), feral swine are responsible for extensive crop damage, the destruction of native ecosystems, and the spread of disease. Purposeful human-mediated movement of feral swine has contributed to their rapid range expansion over the past 30 years. Patterns of deliberate introduction of feral swine have not been well described as populations may be established or augmented through small, undocumented releases. By leveraging an extensive genomic database of 18,789 samples genotyped at 35,141 single nucleotide polymorphisms (SNPs), we used deep neural networks to identify translocated feral swine across the contiguous US. We classified 20% (3364/16,774) of sampled animals as having been translocated and described general patterns of translocation using measures of centrality in a network analysis. These findings unveil extensive movement of feral swine well beyond their dispersal capabilities, including individuals with predicted origins >1000 km away from their sampling locations. Our study provides insight into the patterns of human-mediated movement of feral swine across the US and from Canada to the northern areas of the US. Further, our study validates the use of neural networks for studying the spread of invasive species.
摘要:
全球化导致物种频繁地移出其本地栖息地。这些物种中的一些变得高度入侵,能够深刻地改变入侵的生态系统。野生猪(Susscrofa×domesticus)被认为是最具破坏性的入侵物种之一,人口分布在除南极洲以外的所有大陆。在美国(US),野猪是造成广泛作物损害的原因,本土生态系统的破坏,和疾病的传播。在过去的30年中,野猪的有目的的人类介导的运动有助于其范围的快速扩展。故意引入野猪的模式还没有得到很好的描述,因为种群可以通过小的,无证释放。通过利用广泛的基因组数据库18,789个样本的基因分型为35,141个单核苷酸多态性(SNP),我们使用深度神经网络来识别跨美国邻近区域的易位野猪。我们将20%(3364/16,774)的采样动物分类为易位,并使用网络分析中的中心性度量描述了易位的一般模式。这些发现揭示了野猪的广泛运动远远超出了它们的扩散能力,包括预测起源距离采样地点>1000公里的个体。我们的研究提供了深入了解人类介导的野猪在美国各地以及从加拿大到美国北部地区的运动模式。Further,我们的研究验证了使用神经网络来研究入侵物种的传播。
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