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减少人类疾病风险的重要一步，因为估计60%感染人类的病原体也可以感染动物（包括导致COVID-19的病毒）。 然而，很难预测动物种群中的传染病风险，因为不了解动物环境质量的变化（例如，温度、食物的丰富程度）可能会改变疾病的发病率。 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病媒传播疾病，每年影响30多万人的健康和福祉。 通过与国家生态观测网络的合作，研究小组将测量栖息地质量对美国东北部8个地点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识别莱姆病暴露风险最小化的时间和地点的能力来造福社会，每年直接使数十万人受益。 通过推广，该项目将教育公众了解影响传染病的因素，并为高中生、本科生、研究生和博士后提供重要的培训机会。许多行为和免疫系统特征对环境敏感。但这种变异在多大程度上驱动了自然界中的传染病风险仍然不清楚。例如，尽管已知特定啮齿动物物种是B的关键宿主。虽然莱姆病的病原体是伯氏莱姆病，但目前尚不清楚环境如何影响影响疾病风险的特征。本研究项目将利用国家生态观测网络（氖）的大陆规模来量化B的两种关键啮齿动物宿主的行为和免疫学特征。burgdorferi，一种每年在美国影响超过30万人的病原体。该项目测试的假设是，在环境中的变化夫妇和个体主机特性，改变了丰富的主管主机和驱动出现的疾病热点和冷点。本项目将量化B。在8个氖站点的两种啮齿动物物种中，在3年内检测了莱姆病的感染能力，并使用统计建模将感染易感性的个体差异与非生物和生物环境的差异以及莱姆病风险的差异联系起来。 这种方法的效用将通过使用这些模型来评估，以预测未来几年在额外的氖站点作为当前气候和栖息地条件的函数的疾病流行率。最终，该项目将确定生物-环境相互作用如何驱动个体宿主对感染的易感性，并确定生物组织的水平（例如，个体、种群、物种），在这些水平上，微小的变化会对疾病风险的增加产生重大影响。该奖项由综合有机体系统部的共生、感染和免疫计划以及环境生物学部的宏观系统生物学和氖启用科学计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.