Mathematical Methods for Evaluation of Insect Pest Abundance

Ecosystems are under pressure due to anthropogenic impact which has increased considerably over the last few decades. This pressure can significantly affect the structure of ecological communities, often enhancing population outbreaks of harmful species. Comprehensive ecological monitoring of pest species is therefore necessary in order to provide detailed and timely information about species that can potentially cause problems. It has been increasingly recognized that ecosystems and agro-ecosystems dynamics is essentially multi-scale and its comprehensive understanding is not possible unless the interaction between the processes going on different spatial and temporal scales is taken into account. As far as the data collection is concerned, there are several spatial scales in the pest monitoring problem. The first and smallest spatial scale is related to a single trap. The next spatial scale arises when the local information about the pest density obtained by trapping. A system of N traps is installed in an agricultural field in order to estimate the pest abundance over the field and we refer to this problem as a ‘single field’ problem. The project is to design a mathematical technique in order to investigate the potential importance of the results obtained on the other spatial scales (i.e., the data from the single trap and the data from the landscape scale) for the accurate pest population size evaluation when a single agricultural field is concerned. The approach is based on ideas of numerical integration where one essentially new feature of this evaluation technique is that the characteristic size of the pest species aggregation can be smaller that the distance between neighbouring traps, in which case a probabilistic approach should be used.


Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Mathematics, Applied Mathematics, Theoretical Physics or related subject. Solid knowledge of at least one programming language is required (MATLAB, FORTRAN, C/C++ or similar) and good programming skills are essential. A relevant experience in one or more of the following will be an advantage: the probability theory, ordinary and partial differential equations, stochastic processes, numerical methods.


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