Earth observation to understand and manage nutrition-parasite interactions under global climate change

This project is part of the ONE Planet DTP. Find out more here: https://research.ncl.ac.uk/one-planet/

Global climate change is altering plant phenology, and hence spatio-temporal patterns of primary production in grazing systems. Similar climatic factors drive parasite infectivity on pastures. Because many parasites are tropically acquired from herbage, and in turn influence protein-energy acquisition and utilisation, we might expect impacts of climate change on plants and parasites to have major consequences for the productivity of grazing systems across large scales. The rapid increase in available remotely sensed data on vegetation and climate, along with refinements in simulation modelling of resource and parasite dynamics, provide a timely opportunity to advance predictive understanding of these interactions. In particular, these tools allow for the first time the quantification of expected mismatches between existing grazing patterns and plant phenology, and evaluation of whether seasonality in parasite infection will attenuate or accentuate adverse consequences on grazers. Moreover, attempts by grazers to reconnect with optimal forage availability, e.g. through migration in natural systems or management on livestock farms, might interact with parasite infection patterns to strongly affect net outcomes in terms of protein-energy balance. This project will utilise remotely sensed data and modern dynamic simulation models to evaluate these impacts across large scales, and apply the resulting predictive models to assess consequences for natural and managed grazing systems globally. Outputs will include rational strategies for attenuating negative consequences by conservation and farm management interventions, to protect biodiversity and food security in the most vulnerable areas. Training will be provided in handling of earth observation data, and in building dynamic geographical information systems that combine the latest mechanistic models of parasite infection dynamics and resource partitioning within parasitized host.

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