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

Gilbert, Neil, Madison WI

Investigators

Abstract

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.