The Effect of Anthropogenic Disturbance on the Dynamics of Sin Nombre

人为干扰对 Sin Nombre 动态的影响

• 批准号: 0326999
• 负责人: Maria Denise Dearing
• 金额: $ 182.38万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326999&HistoricalAwards=false
• 关键词: Effect; Anthropogenic; Disturbance; Dynamics; Sin

Sin Nombre hantavirus (SN) is a recently discovered virus carried by deermice that causes disease with high mortality in humans. Several recent studies have proposed that human disturbance of habitat significantly affects the number of deermice infected with SN. Given unprecedented rates of disturbance and limited understanding of the mechanisms governing variation in SN infection, a thorough study of how disturbance affects SN dynamics is warranted. The central focus of the proposed study is to determine how human disturbance affects SN prevalence in deermice and other reservoirs. To address this issue, a multifaceted research program is proposed that includes empirical and theoretical work. The field data will be used to determine the underlying mechanisms responsible for differences in prevalence. These ground-based data will be used to generate predictive mathematical models of prevalence using aerial and satellite images.The broader impacts of this study include education, interdisciplinary research and national security. Several undergraduate and graduate students will be trained as part of this research. The project unites scientists from diverse fields (geography, mathematics, ecology, virology). Lastly, the research will yield critical information on the biology of Sin Nombre virus, which is listed as a biological agent of concern for national security.

Sin Nombre hantavirus (SN) 是最近发现的一种由鹿鼠携带的病毒，可导致人类高死亡率的疾病。 最近的几项研究表明，人类对栖息地的干扰会显着影响感染 SN 的鹿鼠数量。 鉴于前所未有的扰动率以及对 SN 感染变异机制的了解有限，有必要对扰动如何影响 SN 动态进行彻底研究。 本研究的重点是确定人类干扰如何影响鹿鼠和其他宿主中的 SN 流行率。为了解决这个问题，提出了一个多方面的研究计划，包括实证和理论工作。 现场数据将用于确定造成患病率差异的根本机制。 这些地面数据将用于利用航空和卫星图像生成患病率的预测数学模型。这项研究的更广泛影响包括教育、跨学科研究和国家安全。作为这项研究的一部分，一些本科生和研究生将接受培训。 该项目联合了来自不同领域（地理学、数学、生态学、病毒学）的科学家。 最后，这项研究将提供有关 Sin Nombre 病毒生物学的重要信息，该病毒被列为国家安全关注的生物制剂。