Postdoctoral Research Fellowship in Biology: Spatiotemporal Dynamics of Biodiversity–Disease Relationships

这一行动为NSF 2022财年生物学博士后研究奖学金提供了资金，综合研究调查了基因组、环境和表型之间相互作用的生命规则。该奖学金支持研究员的研究和培训，这些研究员将以创新的方式为生活规则领域做出贡献。该项目将促进对疾病风险动态的科学理解。野生动物疾病在自然界中很常见，偶尔会从野生动物“蔓延”到人类种群。尽管这与人类福祉有关，但科学家们对与野生动物疾病的出现和传播相关的环境属性缺乏全面的了解。特别是，人们对生物多样性和疾病风险之间的关系(例如，生物多样性是放大还是稀释疾病风险)知之甚少，也不清楚这种关系可能如何随着持续的全球变化而改变。因此，该项目将应用最先进的建模方法来了解生物多样性和疾病风险之间的动态关系。通过寻求生物多样性-疾病关系的共性并记录它们的背景依赖关系，该项目将加强未来对野生动物疾病对人类健康构成威胁的预测。该项目将利用国家生态观测网络(NEON)关于啮齿动物-壁虱(莱姆病)和鸟类-蚊子(西尼罗河病毒)系统的数据，描述生物多样性与疾病关系的时空变化。特别是，该研究员将调查1)这两个系统的生物多样性-疾病关系是否在空间上具有类似的规模，2)这些关系对生物多样性变化的敏感性，以及3)季节性和气候对生物多样性-疾病关系的时间动态的影响。该研究员将开发多尺度的综合模型，允许将多个数据集(即主机上的数据和载体上的数据)组合成一个连贯的整体。应用在贝叶斯框架中的集成模型将通过估计主体子模型中的宿主生物多样性指标，然后将这些量用作疾病子模型中的预测因子，来实现这种联系。综合模型因其在理解复杂现象方面的有效性而在生物学学科中日益突出，该项目将为未来结合数据集的研究提供信息，以解决疾病生态学中的问题。除了研究，该研究员还将开展职业培训活动(例如，统计研讨会)，并参与扩大科学参与度的活动，包括指导一名本科生进行暑期研究，并领导一个在研究生生态学课程中使用霓虹灯数据的培训模块。该奖项反映了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。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. This project will advance scientific understanding of disease risk dynamics. Wildlife diseases are common in nature and occasionally “spill over” from wildlife to human populations. Despite this link to human well-being, scientists lack a comprehensive understanding of the environmental attributes associated with the emergence and spread of wildlife diseases. In particular, the relationship between biodiversity and disease risk (e.g., whether biodiversity amplifies or dilutes disease risk) is poorly understood, and it is unclear how this relationship might be altered with ongoing global change. Therefore, the project will apply state-of-the-art modeling approaches to understand the dynamic relationship between biodiversity and disease risk. By seeking generalities in biodiversity–disease relationships and documenting their context dependence, the project will enhance future predictions of threats to human health posed by wildlife diseases. The project will characterize spatiotemporal variation in biodiversity–disease relationships using data from the National Ecological Observatory Network (NEON) on rodent–tick (Lyme disease) and bird–mosquito (West Nile virus) systems. In particular, the fellow will investigate 1) whether biodiversity–disease relationships from the two systems scale similarly over space, 2) the sensitivity of these relationships to biodiversity change, and 3) the influence of seasonality and climate on temporal dynamics of biodiversity–disease relationships. The fellow will develop multiscale, integrated models, which allow multiple datasets (i.e., data on hosts as well as vectors) to be combined into a cohesive whole. The integrated model—applied in a Bayesian framework—will accomplish this linkage by estimating host biodiversity metrics in a host submodel and then using these quantities as predictors in a disease submodel. Integrated models are gaining prominence in biological disciplines due to their effectiveness in understanding complex phenomena, and this project will inform future investigations that combine datasets to tackle problems in disease ecology. In addition to the research, the fellow will undertake career training activities (e.g., statistical workshops) and engage in activities to broaden participation in science, including mentoring an undergraduate student in summer research and leading a training module on using NEON data in a graduate ecology course.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.