Christina Cobbold is Professor of Mathematical Biology and member of the Boyd Orr Centre for Ecosystem and population Health at the University of Glasgow, UK . She is a mathematician working at the interface of population ecology and vector ecology and together with collaborators her research focusses on developing novel mathematical frameworks and predictive tools for understanding species population dynamic responses to environmental change. She is broadly interested in understanding how climate change, land use change, and other anthropogenic influences alter the dynamical interactions that shape ecosystems. Current efforts focus on how global change interacts with phenotypic plasticity in determining population dynamics, extinction risk, spread of disease.
The role of individuals and their traits in determining the impacts of environmental change: from blowflies to mosquitoes
Environmental change is having profound effects on populations, from dramatic global declines in biodiversity to increased incidence and geographical spread of vector borne diseases, such as dengue and chikungunya. Predicting complex species-environment interactions is crucial for guiding conservation and disease mitigation strategies in a dynamically changing world. Many species can rapidly respond to their changing environment through phenotypic plasticity, where variable traits are expressed depending on environmental conditions experienced. For individuals, the effects of phenotypic plasticity can be quantified by measuring environment–trait relationships, but it is often difficult to predict how phenotypic plasticity affects dynamics at the level of the population. I will present a mathematical framework for capturing the interaction of environment, individuals and their traits to establish the role of phenotypic plasticity in mitigating the effects of climate change. I will show how this new mathematical framework leads to both unexpected mathematical questions and novel dynamics. When used to study the vector borne disease, dengue, spread by mosquitoes, we find that environmental variation drives a dynamic phenotypic structure in the mosquito population, which accurately predicts global patterns of mosquito trait-abundance dynamics. In turn, this interacts with disease transmission to capture historic dengue outbreaks. By comparing the model to a suite of simpler models, we reveal that it is the delayed phenotypic plasticity that is critical for accurate prediction of the location, magnitude and timing of historical and recent dengue outbreaks.
