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首页> 外文期刊>South African Journal of Science >Understanding pathogen transmission dynamics in waterbird communities: At what scale should interactions be studied?
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Understanding pathogen transmission dynamics in waterbird communities: At what scale should interactions be studied?

机译:了解水鸟群落中病原体的传播动态:应在多大程度上研究相互作用?

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Pathogen transmission in animal populations is contingent on interactions between and within species. Often standard ornithological data (e.g. total counts at a wetland) are the only data available for assessing the risks of avian pathogen transmission. In this paper we ask whether these data can be used to infer fine-scale transmission patterns. We tested for non-randomness in waterbird assemblages and explored waterbird interactions using social network analysis. Certain network parameter values were then compared to a data set on avian influenza prevalence in southern Africa. Our results showed that species associations were strongly non-random, implying that most standard ornithological data sets would not provide adequate information on which to base models of pathogen spread. In both aquatic and terrestrial networks, all species regularly associated closely with other network members. The spread of pathogens through the community could thus be rapid. Network analysis together with detailed, fine-scale observations offers a promising avenue for further research and management-oriented applications.
机译:动物种群中的病原体传播取决于物种之间和物种内部的相互作用。通常,标准鸟类学数据(例如湿地的总计数)是唯一可用于评估禽病原体传播风险的数据。在本文中,我们问这些数据是否可用于推断精细尺度的传输模式。我们测试了水鸟组合中的非随机性,并使用社交网络分析探索了水鸟互动。然后将某些网络参数值与南部非洲禽流感流行率的数据集进行比较。我们的结果表明,物种之间的关联是高度非随机的,这意味着大多数标准鸟类学数据集都无法提供足够的信息来作为病原体传播模型的基础。在水生和陆地网络中,所有物种都经常与其他网络成员密切相关。因此,病原体在社区中的传播可能很快。网络分析以及详细,精细的观察结果为进一步研究和面向管理的应用程序提供了广阔的前景。

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