IMAGINE Collaborative Research: Linking individual variation in immunity and behavior to landscape patterns in disease risk using the National Ecological Observatory Network (NEON)

了解减少动物感染的因素是降低人类疾病风险的重要一步，因为据估计，感染人类的病原体中有60%也可以感染动物(包括导致新冠肺炎的病毒)。然而，很难预测动物种群中的传染病风险，因为人们不知道动物环境质量的变化(如温度、食物丰度)如何改变发病率。该项目将调查栖息地质量的变化如何影响莱姆病的生态动态，莱姆病是美国最常见的媒介传播疾病，每年影响超过30万人的健康和福祉。通过与国家生态观测网络的合作，研究小组将在美国东北部的8个地点测量栖息地质量对小鼠行为和免疫系统特征的影响，并开发统计模型，将这些动物数据与莱姆病风险联系起来。该项目将通过提高我们确定可将莱姆病风险降至最低的时间和地点的能力而造福社会，每年直接使数十万人受益。通过外展，该项目将教育公众有关影响传染病的因素，并为培训在STEM领域代表性不足的高中生、本科生、研究生和博士后提供重要机会。许多行为和免疫系统特征对环境很敏感。但这种变异在多大程度上导致了自然界中的传染病风险，目前仍不清楚。例如，虽然已知特定啮齿动物物种是莱姆病的病原体伯氏杆菌的关键宿主，但环境如何影响影响疾病风险的特征尚不清楚。这项研究项目将利用国家生态观测网络(NEON)的大陆规模来量化伯氏杆菌的两种关键啮齿动物宿主的行为和免疫学特征。伯氏杆菌是一种每年在美国影响超过30万人的病原体。这个项目测试了这样一种假设，即环境的变化结合和分离了个体宿主的特征，改变了有能力的宿主的丰度，并推动了疾病热点和冷点的出现。该项目将在3年内量化两种啮齿动物在8个霓虹灯地点的伯氏杆菌能力，并使用统计模型将感染易感性的个体差异与非生物和生物环境的变化以及莱姆病风险的变化联系起来。将通过使用这些模型根据未来几年更多霓虹灯站点的当前气候和栖息地条件来预测疾病流行情况，以评估这一方法的实用性。最终，该项目将确定生物-环境相互作用如何导致个体宿主对感染的易感性，还将确定生物组织(如个人、种群、物种)的水平(S)，在这种水平上，微小的变化会导致疾病风险的增加。该奖项由综合组织系统分部的共生、感染和免疫计划和环境生物学分部的大系统生物学和霓虹灯启动科学计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding the factors that reduce infections in animals is an essential step towards reducing human disease risk because an estimated 60% of pathogens that infect humans can also infect animals (including the virus that causes COVID-19). However, it is difficult to predict infectious disease risk in animal populations because it is not understood how changes in the quality of an animal’s environment (e.g., temperature, abundance of food) may change disease rates. This project will investigate how variation in habitat quality affects the ecological dynamics of Lyme disease, the most common vector-borne disease in the US, affecting the health and well-being of over 300,000 people annually. Through a collaboration with the National Ecological Observatory Network, the research team will measure the effects of habitat quality on the behavioral and immune system traits of mice at 8 sites across the northeastern U.S. and develop statistical models to link these animal data to Lyme disease risk. This project will benefit society by improving our ability to identify the times and places where risk of Lyme disease exposure can be minimized, directly benefitting hundreds of thousands of people each year. Through outreach, the project will educate the public about the factors that affect infectious disease and provide significant opportunities for training of high-school students, undergraduates, graduate students, and postdocs underrepresented in STEM fields.Many behavioral and immune system traits are sensitive to the environment. But the extent to which this variation drives infectious disease risk in nature remains obscure. For example, although it is known that particular rodent species are critical hosts of B. burgdorferi, the causative agent of Lyme disease, it is unknown how the environment affects traits that influence disease risk. This research project will leverage the continental scale of the National Ecological Observatory Network (NEON) to quantify behavioral and immunological traits of two keystone rodent hosts for B. burgdorferi, a pathogen that affects over 300,000 people in the U.S. each year. This project tests the hypothesis that variation in the environment couples and decouples individual host traits, altering the abundance of competent hosts and driving the emergence of disease hot and cold spots. This project will quantify B. burgdorferi competence in two rodent species across 8 NEON sites over 3 years, and use statistical modeling to link individual variation in infection susceptibility to variation in the abiotic and biotic environment and hence variation in Lyme disease risk. The utility of this approach will be evaluated by using these models to forecast disease prevalence as a function of current climatic and habitat conditions at additional NEON sites in future years. Ultimately, the project will identify how organism-environment interactions drive the susceptibility of individual hosts to infection, and will also identify the level(s) of biological organization (e.g., individuals, populations, species) at which small changes have large consequences for increased disease risk. This award was co-funded by the Symbiosis, Infection and Immunity Program in the Division of Integrative Organismal Systems and the Macrosystems Biology and NEON Enabled Science Program in the Division of Environmental Biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.