The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

Novel models for invasive species' management
Globalisation is increasing rates of species transportation beyond their native ranges, including highly invasive animal and plant species and devastating plant pathogens that impact biodiversity and society. To mitigate this threat, policy makers require general strategies for slowing or even eradicating invading species. However, management decisions are usually made under considerable uncertainty, including a lack of knowledge about the precise distribution, dispersal ability and population dynamics of the species. Designing effective management strategies under this uncertainty remains a challenge that has not been fully addressed by ecological and epidemiological modelling. The goal of this studentship is to use spatially-explicit simulation models to identify effective and efficient control strategies across an uncertain range of invasion scenarios. Through integrated modelling of invasive species dynamics and management, the research will address important questions, including: (1) How well do different strategies meet different management objectives? (2) What is the value of information, in other words how does better knowledge of the invasion translate to more effective management? (3) Do invasions driven by different processes require different management approaches? The student will develop computational models for the management of invading species, based on rules for distributing surveillance and control effort in space and time. Management effectiveness will be evaluated by linking the management model to ecological models for invasive species spread. We plan to compare the effectiveness of various fixed management strategies with adaptive management. In an adaptive approach, managers reduce uncertainty by learning about the invasion process from data collected as it progresses, and use this information to refine management decisions. In our simulations, this will be achieved using machine learning to fit simple models to the simulated invasion process. In the real world, the data for learning may come from the species surveillance scheme itself, as well as from the general public via citizen science. Therefore, we will allow the models to exploit both kinds of information. To include uncertainty from biological complexity, the student will extend generic invasion models by including processes that are often observed in real invasions but typically ignored in theoretical models, such as multiple and repeated introduction events, multiple dispersal mechanisms, or evolution of increased dispersal during range expansion. To parameterise these models realistically, the student will conduct statistical analysis of high quality spatio-temporal invasion data for insects, plants and/or plant pathogens (e.g. Harmonia axyridis, Impatiens glandulifera, Xylella fastidiosa). Overall, the research will demonstrate the value of information for the effective management of invasive species and provide new solutions for reducing their impacts. Further reading: White, Bullock, Hooftman, Chapman (2017) Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy. Biological Invasions 19, 1825-1837. Carrasco, Mumford, et al (2010) Comprehensive bioeconomic modelling of multiple harmful non-indigenous species. Ecological Economics 69, 1303-1312. Shea et al (2014) Adaptive management and the value of information: Learning via intervention in epidemiology. PLOS Biology 12, e1001970.
John Mumford
Daniel Chapman
James Bullock (jmbul@ceh.ac.uk); Steven White (CEH, smwhit@ceh.ac.uk)
Development of mathematical theory, Computing, Quantitative data analysis, Ecological observations / data collection
John Mumford
Full training will be given in (1) computational simulation of species invasion processes and management strategies; (2) Simulation of learning during adaptive management using machine learning algorithms; (3) Statistical modelling of spatio-temporal data from real invasions (e.g. fitting dispersal kernels with Bayesian modelling).
The student will develop novel algorithms for adaptive management of invasive species. This includes extending simple ecological models for invasion, using machine learning to model adaptive management and integrating the ecological and management models to simulate control of invasions.
Spatial population dynamics of invasive species and emerging diseases, including responses to management and, potentially, rapid evolution of increased dispersal during range expansion.
The project will provide guidance for developing robust and efficient spatial management strategies using uncertain knowledge about invasive species and emerging diseases. This will be relevant for existing management by government agencies nationally (e.g. UK rapid response to Asian hornet) and at multi-national scales (e.g. EU response to the Xylella fastidiosa emergency).
Current theory for managing invasive species and emerging diseases is largely based on highly simplistic models for management and invasion dynamics. This project takes the field forward by showing how to devise adaptive management strategies using uncertain knowledge on more complex and realistic invasion processes.
Modelling invasion dynamics and adaptive management strategies means the project spans ecology, epidemiology, socio-ecological systems and economics.
Population ecology, Ecological/Evolutionary tools, technology & methods
Training in ecological and epidemiological theory relevant for invasive species, simulation modelling using high performance computing clusters, statistical analysis of spatio-temporal distribution data (R, MATLAB, GIS) and design of simulation experiments. There will be opportunities to participate in an EU-funded international research and training network on Xylella fastidiosa invasion.
Imperial College London, Silwood Park Campus, Ascot, with some time spent at CEH in Edinburgh or Wallingford
No
2017-10-02 13:43:01