野生动物行为记录与理解的创新技术博士研究员

Emerging infectious diseases pose substantial threats to the health of wildlife, livestock, humans and ecosystems. Wildlife serve as important reservoirs and vectors for a diverse range of microorganisms, including viruses, bacteria, fungi, and parasites. However, pathogen transmission dynamics within wildlife populations and between wildlife and their environments remain poorly understood because transmission pathways are complex, and vary in both space and time. Metagenomic sequencing allows us to generate huge volumes of data on the composition and dynamics of host and environment-associated microbiomes, including the profiling of pathogens but the sheer complexity of these data make identifying causal pathways and ecological mechanisms shaping disease transmission difficult.

This interdisciplinary PhD project aims to integrate environmental and host-associated metagenomic data with state-of-the-art machine learning approaches to identify patterns of pathogen transmission in wildlife populations, including migratory birds and badgers. By combining genomic, ecological, and environmental datasets, the project will develop predictive models capable of detecting transmission hotspots, identifying key environmental reservoirs, and forecasting pathogen spread under changing environmental conditions.

Research Objectives

The project will address three primary objectives:

• Characterise microbial and pathogen diversity in model wildlife populations and associated environmental samples, including water, soil, sediment, and foraging habitats, using high throughput metagenomic sequencing.

• Identify transmission pathways and ecological drivers by linking microbial community composition to host traits, movement behaviour, habitat use, seasonality, and environmental conditions.

• Develop machine learning pipelines that include appropriate preprocessing of the various datasets, feature engineering and (predictive) model / feature selection. We will use this pipeline to infer likely transmission networks and predict pathogen occurrence, persistence, and spread across landscapes. We will use metagenomic data to identify taxa, functional genes, and potential pathogens. We will then combine metagenomic & Engineering and Physical Sciences Research Council Doctoral Landscape Award environmental with supervised machine learning approaches (e.g. Random Forests, Gradient Boosting Machines, and Deep Neural Networks) to predict pathogen occurrence and transmission risk. We will also employ Multiomics Factor Analysis (MOFA) to identify covariance between metagenomic and environmental data, and network analysis to identify putative transmission pathways.

Expected Outcome and Impact:

The project will generate fundamental insights into how pathogens circulate within wildlife systems and the role of environmental reservoirs in maintaining disease transmission. By integrating machine learning with metagenomic surveillance, the research will provide novel analytical frameworks for identifying transmission pathways that are difficult to observe directly. The findings will contribute to wildlife disease ecology, conservation biology, and One Health research by improving understanding of the links between environmental change, biodiversity, and infectious disease dynamics. The predictive tools developed during the project could support disease surveillance programmes, inform targeted management interventions, and enhance preparedness for emerging pathogens affecting wildlife, livestock, and human populations.

Funded by: EPSRC Engineering and Physical Sciences Research Council £21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.