LTREB: Acorn pulses and the dynamics of rodents, ticks, and Lyme-disease risk in oak forests

传播重要人类疾病的节肢动物病媒数量的长期数据对于预测风险是必要的。 人类接触莱姆病的风险与感染伯氏疏螺旋体（病原体）的黑腿蜱（载体）的丰度有关。 这项研究的目的是了解在美国东北部的代表性网站的风险急剧变化的原因先前的研究表明，莱姆病的风险与先前丰富的白色足小鼠（蜱虫和病原体的关键宿主），和小鼠丰度与先前丰富的橡子（小鼠的重要食物）。 蜱虫和老鼠的数量以及橡子的产量可能受到气候条件变化的影响。 拟议的研究将确定哪些气候变量影响蜱虫生存，小鼠丰度和橡子产量。 它将澄清橡子和老鼠之间的关系，以及老鼠和蜱虫之间的关系。 研究结果将有助于将气候变化与森林健康相结合，以预测风险。 这个长期项目的结果用于K-12课堂教学（包括PBS NOVA单元），本科教育（独立研究项目和生态学，保护生物学和哺乳动物学教科书材料）和公共教育（关于莱姆病风险因素的地方和区域介绍，报纸和杂志的媒体报道）。这项研究具有很强的实际意义，因为它可以预测疾病高风险的时间和地点。

Long-term data on the abundance of arthropod vectors that transmit important human diseases are necessary to predict risk. Risk of human exposure to Lyme disease is related to the abundance of blacklegged ticks (the vector) that are infected with Borrelia burgdorferi (the pathogen). The objective of this research is to understand the causes of dramatic variation in risk at representative sites in the northeastern U.S.A. Prior research suggests that Lyme disease risk is correlated with prior abundance of white-footed mice (a critical host for ticks and pathogens), and that mouse abundance is correlated with prior abundance of acorns (a crucial food for mice). Tick and mouse populations, and acorn production, are potentially influenced by changing climatic conditions. The proposed research will establish which climatic variables influence tick survival, mouse abundance, and acorn production. It will clarify the nature of the relationships between acorns and mice, and between mice and ticks. The results will facilitate the integration of climate change with forest health to predict risk. Results of this long-term project are used for K-12 classroom teaching (including a PBS NOVA unit), undergraduate education (independent research projects and material for textbooks in ecology, conservation biology, and mammalogy), and public education (local and regional presentations about Lyme disease risk factors, media coverage in newspapers and magazines). The proposed research has strong practical implications because it allows prediction of times and places of high disease risk.