Coherent Modelling of Collective Movement
Many species of ecological and economic importance exhibit collective movement, often in large groups or herds and on a large geographical scale. Detailed understanding of the patterns and mechanisms of such movement is therefore important, not least for the prediction of responses to environmental change and human activity. Large scale deployment of GPS tags is beginning to generate the simultaneous location data necessary for such understanding. However, conventional modelling, statistical and computational techniques are inadequate; most work in this area focusses on local interactions without a coherent overall model of movement behaviour by observed and unobserved individuals, or is limited to very small scale studies or purely theoretical models that cannot readily be matched with data. This project will build on recent advances in movement modelling in heterogeneous environments, Bayesian statistical methods and efficient computational algorithms, to develop tools for statistically sound inference about large-scale collective movement from simultaneously tagged animals. Lead supervisor Paul Blackwell (SoMaS, University of Sheffield) will provide statistical, computational and modelling expertise; co-supervisor Anna Skarin (Swedish University of Agricultural Sciences) will provide expertise on ecology, behaviour and interactions with human activity, as well as access to data for a case study on reindeer.
How to apply: Go to http://www.sheffield.ac.uk/postgraduate/research/apply/applying after reading the information contained on that page click the link to the Postgraduate online application form
This project will be funded by the Leverhulme Trust Centre for Advanced Biological Modelling