MCA: Physiology-based mechanistic models of vector fitness to forecast species responses to coarse- and fine scale anthropogenic environmental change

MCA：基于生理学的矢量适应性机制模型，用于预测物种对粗尺度和细尺度人为环境变化的反应

• 批准号: 2322213
• 负责人: Gideon Wasserberg
• 金额: $ 28.32万
• 依托单位: University of North Carolina Greensboro
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322213&HistoricalAwards=false
• 关键词: MCA; Physiology; based; mechanistic; models

Emerging infectious diseases may be from new disease in a population or have existed previously but are rapidly increasing in incidence or geographic range. Many new diseases are associated with spillover from a wildlife source into human populations. Anthropogenic (human caused) climatic- and land-use changes are often thought to be key drivers of disease emergence. This is especially true for arthropod vectors that are particularly sensitive to small changes in climatic and microclimatic conditions. Therefore, the prevention of such outbreaks necessitates advanced predictive models. Most current distribution models are based on correlations rather than on causal mechanistic relationships. Physiology-based mechanistic models hold a promise to overcome the limitations of correlational models by capturing specific biophysical signals linking life-history traits (e.g., larval development, mortality, growth) with environmental variables. Using the sand fly Phlebotomus papatasi as a case study, this project will develop a mechanistic model and will field-test its predictions, with clear public health implications in terms of reducing human exposure for the pathogens these sand flies transmit, including Old-World cutaneous leishmaniasis and pappataci fever being the most significant ones. In addition, this project will advance the theoretical and analytical frameworks of the emerging field of disease biogeography applied to a vector-borne disease in a changing world. This project will provide training for students and professional development opportunities for faculty. This project will use comprehensive physiological lab experimentation to build mechanistic models of fitness to forecast vector species responses to coarse- and fine-scale environmental change. The specific aims are to: (1) determine the physiological parameters that make disease vectors more sensitive to global climate change and influence their geographical spread and transmission potential (coarse scale) and (2) determine the role of microclimatic variation due to land use modification on the metapopulation dynamics of vector-borne disease systems (fine scale). Specifically, this project will integrate mechanistic and correlative ecological niche models to reconstruct the fitness of the sand fly and the distributional ecology of the transmission. Mathematical and machine learning models will be calibrated using laboratory (physiological experimentation) and field-collected (occurrences) data coupled with environmental variables. The project will study in detail the structure, size, and position of the ecological niche of P. papatasi by manipulating critical environmental variables manifested at coarse (e.g., temperature, relative humidity, photoperiod) and fine (e.g., soil temperature, moisture, organic matter) spatial scales. Field sampling will be conducted in Israel to test the predictions of the model.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.

新出现的传染病可能来自人群中的新疾病或以前存在过，但在发病率或地理范围上正在迅速增加。许多新的疾病都与野生动物来源对人类种群的溢出有关。人为的(人为的)气候和土地利用变化通常被认为是疾病出现的关键驱动因素。对于对气候和小气候条件的微小变化特别敏感的节肢动物媒介来说，情况尤其如此。因此，预防此类疫情的爆发需要先进的预测模型。目前的大多数分布模型都是基于相关性，而不是因果机制关系。基于生理学的机制模型有望通过捕捉将生活史特征(如幼虫发育、死亡率、生长)与环境变量联系起来的特定生物物理信号来克服相关模型的局限性。以沙蝇白纹伊蚊为例，该项目将开发一个机械模型，并对其预测进行现场测试，在减少人类接触这些沙蝇传播的病原体方面具有明确的公共卫生影响，包括东半球皮肤利什曼病和木瓜热是最重要的病原体。此外，该项目将推进新兴疾病生物地理学领域的理论和分析框架，该领域适用于不断变化的世界中的病媒传播疾病。该项目将为学生提供培训，为教师提供职业发展机会。该项目将使用全面的生理学实验室实验来建立适应机制模型，以预测媒介物种对粗细尺度环境变化的反应。其具体目标是：(1)确定使病媒对全球气候变化更加敏感并影响其地理传播和传播潜力的生理参数(粗尺度)和(2)确定土地利用变化引起的小气候变化对病媒传播疾病系统集合种群动态的作用(细尺度)。具体地说，该项目将整合机械学和相关的生态位模型，以重建沙蝇的适合度和传播的分布生态。数学和机器学习模型将使用实验室(生理实验)和现场收集(发生)的数据以及环境变量进行校准。该项目将通过操纵在粗(如温度、相对湿度、光周期)和细(如土壤温度、水分、有机质)空间尺度上表现的关键环境变量，详细研究帕帕塔西的生态位的结构、大小和位置。将在以色列进行实地抽样，以测试模型的预测。这一奖项反映了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。