• University of Glasgow Featured PhD Programmes
  • University College London Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • FindA University Ltd Featured PhD Programmes
  • Heriot-Watt University Featured PhD Programmes
  • UNSW Australia Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
University of Sheffield Featured PhD Programmes
FindA University Ltd Featured PhD Programmes
University of Southampton Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of Bristol Featured PhD Programmes

Risk CDT - Untangling Risk and Uncertainty in Socio-ecological Networks

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr L Robinson
    Dr Peter Green
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description


This is a project within the multi-disciplinary EPSRC and ESRC Centre for Doctoral Training (CDT) on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments, within the Institute for Risk and Uncertainty. The studentship is granted for 4 years and includes, in the first year, a Master in Decision Making under Risk & Uncertainty. The project includes extensive collaboration with prime industry to build an optimal basis for employability.

The network of interactions that underlies any socio-ecological system is complex (e.g. Knights et al., 2013). Yet it is recognised that without a better understanding of how humans interact with natural ecosystems, we will continue to fail to operationalise sustainable ecosystem-based management (Leslie & McLeod, 2007; Tallis et al., 2010). The interactions between humans and the environment can be represented by a network of nodes and linkages that should encompass all pathways, including the benefits that humans derive from natural ecosystems (e.g. food, climate regulation, aesthetic value) and the impacts they exert on them (e.g. pollution, resource depletion, habitat destruction). At the same time, this network of interactions sits within a broader system subject to macro-level environmental (e.g. storms, rainfall, temperature), economic (e.g. oil prices) and political drivers (e.g. security, migration) that are highly stochastic in nature.

In this PhD, the student will work with an existing socio-ecological network developed by the lead supervisor (Robinson) that represents the human-ecosystem interactions in four European regional seas. To date, only relatively simple analyses have been undertaken, including:

1. exploring connectance and linkage strength in the network (Knights et al., 2013);
2. developing a risk assessment of a subset of the interactions (Knights et al., 2014), and;
3. management strategy evaluation based on reducing risk through the network (Piet et al., 2015).

In this PhD, we will work to further develop the network so that, (1) it has the capacity to explore scenarios that account for realistic variability in external drivers (e.g. environment, socio-cultural, economic and political drivers), and (2) it can respond to positive and negative feedback within the network (e.g. due to food web interactions, or feedback loops from use of ecological services to management of human activities). The student will use graphical modelling techniques (Bishop, 2006; MacKay, 2003) to build on the current state of the art. This will, for example, involve the application of Bayesian networks (Pearl, 2000) which will allow the complex interactions present in socio-ecological networks to be analysed within a probabilistic framework. This will be combined with advanced elicitation techniques ((Moala et al., 2010), for example) which can be used to quantify the uncertainties associated with expert judgements. Ultimately, the project aims to explore and identify critical pathways in the network, where change might result in severe consequences for either the environment or society. This will involve working with Robinson and Green, as well as Crego, who has extensive experience in the development and application of methodologies which can be used to explore critical incidents, conduct probabilistic analyses and aid decision-making under uncertainty.

Suitability of candidates: The project requires a strong numerical background and a keen interest to work at the interface of environmental, modelling and statistical approaches. Students must at least have an A Level (or equivalent) in mathematics. Applicants should have a degree in Ecological or Environment Sciences or in relevant fields of the Applied Mathematical Sciences. A combined education in these fields would be a significant advantage. An interest in Marine Ecosystems and sustainable use of the environment is desirable. The results of this work will be of direct relevance to those who advise government (local, national and international) on decision-making around key environmental regulations and policy that require the management of human activities.

Funding Notes

The PhD Studentship (Tuition fees + stipend of £ 14,296 annually over 4 years) is available for Home/EU students. In addition, a budget for use in own responsibility will be provided.


- Bishop, C. M. 2006. Pattern recognition and machine learning. Springer.
- Knights A.M., Koss R.S. and Robinson, L.A. 2013. Identifying common pressure pathways from a complex network of human activities to support ecosystem-based management. Ecological Applications 23(4), 755-765
- Knights, A. M., Piet, G. J., Jongbloed, R.H., Tamis, J.E., White, L., Akoglu, E., Boicenco, L., Churilova, T., Kryvenko, O., Fleming-Lehtinen, V., Leppanen, J-M., Galil, B. S., Goodsir, F., Goren, M., Margonski, P., Moncheva, S., Oguz, T., Papadopoulou, K. N., Setala, O., Smith, C. J., Stefanova, K., Timofte, F. and Robinson, L. A. 2015 An exposure-effect approach for evaluating ecosystem-wide risk from human activities. ICES Journal of Marine Science, 72(3): 1105-1115.
- Leslie, H. M. and McLeod, K. L. 2007. Confronting the challenges of implementing marine ecosystem-based management. Frontiers in Ecology and the Environment, 5: 540–548.
- MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge University Press.
- Moala, F. A., and O’Hagan, A. (2010). Elicitation of multivariate prior distributions: A nonparametric Bayesian approach. Journal of Statistical Planning and Inference, 140(7), 1635-1655.
- Pearl, J. (2000). Bayesian Networks. UCLA Congnitive Systems Laboratory. Technical Report.
- Piet , G.J., Jongbloed, R. H., Knights, A. M., Tamis, J. E., Paijmans, A. J., van der Sluis, M. T., de Vries, P. and Robinson, L.A. 2015. Evaluation of ecosystem-based marine management strategies based on risk assessment. Biological Conservation 186, 158-166.
- Tallis, H., Levin, P. S., Ruckelshaus, M., Lester, S. E., McLeod, K. L.,Fluharty, D. L., and Halpern, B. S. 2010. The many faces of ecosystem-based management: making the process work today in real places. Marine Policy, 34: 340–348.
- White, L.J., Koss, R.S., Knights, A.M., Eriksson, A. and Robinson L.A. 2013. ODEMM Linkage Framework Userguide (Version 2). ODEMM Guidance Document Series. No.3. EC FP7 Project (244273) ‘Options for Delivering Ecosystem-based Marine Management’. University of Liverpool.

Share this page:

Cookie Policy    X