Predicting the impact of climate change on tick populations- a dynamic GIS approach - presented by Prof Rachel Norman FRSE and Dr Sara Gandy

Predicting the impact of climate change on tick populations- a dynamic GIS approach

Prof Rachel Norman FRSE

Prof Rachel Norman FRSE
Journal of The Royal Society Interface

Associated Journal of The Royal Society Interface article

A. J. Worton et al. (2024) GIS-ODE: linking dynamic population models with GIS to predict pathogen vector abundance across a country under climate change scenarios. Journal of The Royal Society Interface
Article of record
Predicting the impact of climate change on tick populations- a dynamic GIS approach
Prof Rachel Norman FRSE
Rachel Norman
University of Stirling
Chaired by Sara Gandy

Mechanistic mathematical models such as ordinary differential equations (ODEs) have a long history for their use in describing population dynamics and determining estimates of key parameters that summarize the potential growth or decline of a population over time. More recently, geographic information systems (GIS) have become important tools to provide a visual representation of statistically determined parameters and environmental features over space. Here, we combine these tools to form a ‘GIS-ODE’ approach to generate spatiotemporal maps predicting how projected changes in thermal climate may affect population densities and, uniquely, population dynamics of Ixodes ricinus, an important tick vector of several human pathogens. Assuming habitat and host densities are not greatly affected by climate warming, the GIS-ODE model predicted that, even under the lowest projected temperature increase, I. ricinus nymph densities could increase by 26–99% in Scotland, depending on the habitat and climate of the location. Our GIS-ODE model provides the vector-borne disease research community with a framework option to produce predictive, spatially explicit risk maps based on a mechanistic understanding of vector and vector-borne disease transmission dynamics.

References
  • 1.
    A. J. Worton et al. (2024) GIS-ODE: linking dynamic population models with GIS to predict pathogen vector abundance across a country under climate change scenarios. Journal of The Royal Society Interface
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R. Norman (2024, November 1), Predicting the impact of climate change on tick populations- a dynamic GIS approach
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Video length 59:30
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