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.

新兴的传染病可能来自人群中的新疾病，或者以前存在，但发病率或地理范围正在迅速增加。许多新的疾病与野生动植物来源的溢出有关。人为（人为引起的）气候变化通常被认为是疾病出现的主要驱动因素。对于对气候和微气候条件的小变化特别敏感的节肢动物矢量尤其如此。因此，预防此类爆发需要先进的预测模型。大多数当前的分布模型基于相关性而不是因果机械关系。基于生理学的机械模型通过捕获将生活历史特征（例如幼虫发展，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。该项目将Sand Fly Phlebotomus papatasi作为案例研究，将开发机械模型，并将对其预测进行现场测试，在减少人类对病原体的暴露量的明显影响中，这些沙蝇传播，包括旧世界的皮肤皮肤病和Pappataci发烧。此外，该项目将推进新兴生物地理学领域的理论和分析框架应用于不断变化的世界中媒介疾病的生物地理领域。该项目将为学生提供培训，并为教师提供专业发展机会。该项目将使用全面的生理实验室实验来构建适应性的机械模型，以预测载体物种对粗尺度和细尺度环境变化的反应。具体目的是：（1）确定使疾病媒介对全球气候变化更加敏感的生理参数，并影响其地理传播和传播潜力（粗尺度），以及（2）确定由于土地利用修饰在媒介 - 疾病疾病系统的体型动力学方面而引起的微气候变化的作用（量表）。具体而言，该项目将整合机械和相关的生态位模型，以重建沙蝇的适应性和传播的分布生态。数学和机器学习模型将使用实验室（生理实验）和现场收集（出现）数据与环境变量进行校准。该项目将通过操纵在粗糙的（例如温度，相对湿度，光周期）和细（例如，土壤温度，水分，水分，有机物）空间尺度的临界环境变量来详细研究P. papatasi生态位的结构，大小和位置。该奖项将在以色列进行现场抽样，以测试该模型的预测。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。