Research
My research seeks to understand the broad scale ecology and evolution of species interactions. I am particularly interested in developing theory of how host-specificity influences infectious disease, and generating methods for predicton, monitoring, and synthesizing data on cross-species infections. I am energized by collaborative science, which has led me to work on a variety of topics, but my core research falls into three themes:
1) Macroecology of infectious diseases
Macroecology identifies patterns and processes at the broadest scales of organization. This involves analyses of comparative data across large spatial and temporal scales, and among all branches of the tree of life. Over the past decade, studies from diverse host-pathogen systems show that cross-species pathogen sharing is constrained by evolutionary relationships among hosts. Through the application of phylogenetic comparative methods and Bayesian statistics, I use evolutionary trees to evaluate macro-scale patterns of pathogen sharing across hosts, analyze biogeographic patterns of pathogens, and predict undocumented host-pathogen interactions.
2) Causes and consequences of host specificity
Most infectious organisms can infect multiple species. Some infections rarely result in death, whereas others are nearly always fatal. For multi-host pathogens, the severeity of disease may be subject to multiple trade-offs, including the ecologies and evolutionary histories of their host species. I have used evolutionary theory to develop statistical models of mortality among high-priority domesticated animal pathogens, and investigate whether bats have unique tolerance to viral disease compared to other viral reservoirs.
3) Next generation tools for biodiversity science
Approaches that allow us to synthesise information on host-pathogen interactions can be used to identify potential reservoirs of high-consequence pathogens, efficiently expand existing global infectious disease databases, and prioritize future research. Often macroecological models of infectious disease are limited by a lack of comparative data on infectious disease outcomes. Current global host-pathogen databases are compiled via time-intensive manual searches of the primary literature, but as the volume of new papers expands exponentially, they can become rapidly outdated. I am exploring new approaches to address this using text mining via Natural Language Processing (NLP) and Large Language Models (LLMs).
However, in many regions considered to have a high risk of harbouring future emerging diseases, even baseline surveys of biological diversity are lacking. I worked to asses the utility of environmental DNA for monitoring both hosts and potential pathogens in the watering holes in the Kruger National Park, South Africa, and am adapting these approaches to study the antimicrobal potential of seagrass microbiomes. In addition to exploring genomic landscapes, my students and I are applying Protein Language Models (pLMs) to the study of virulence evolution, immmunology, and microbial ecology.
