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

新发传染病可能来自人群中的新疾病，或者以前已经存在，但发病率或地理范围迅速增加。许多新的疾病都与从野生动物传播到人类有关。人为（人类引起的）气候和土地使用变化往往被认为是疾病出现的主要驱动因素。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病媒来说，情况尤其如此。因此，预防此类疫情需要先进的预测模型。大多数当前的分配模型是基于相关性，而不是因果机械关系。基于生理学的机制模型有望通过捕获连接生活史性状的特定生物物理信号（例如，幼虫发育、死亡率、生长）与环境变量。该项目将以沙蝇Phlebotomus papatasi为案例研究，开发一个机制模型，并对其预测进行实地测试，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，包括旧世界皮肤利什曼病和帕帕塔西热是最重要的病原体。此外，该项目将推进新兴疾病地理学领域的理论和分析框架，应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，为教师提供专业发展机会。该项目将使用综合生理实验室实验来建立适应性的机械模型，以预测病媒物种对粗尺度和细尺度环境变化的反应。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扩散和传播潜力的生理参数（粗比例）;（2）确定因土地利用改变而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（细比例）。具体而言，本计画将整合机制与相关生态位模式，以重建白蛉的适合度与传播的分布生态。数学和机器学习模型将使用实验室（生理实验）和现场收集的（发生）数据以及环境变量进行校准。该项目将通过操纵粗略显示的关键环境变量（例如，温度，相对湿度，光周期）和精细（例如，土壤温度、湿度、有机质）空间尺度。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。

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.