University of Exeter funding: NERC GW4+ DTP PhD studentship

Bridging the gap between structure and function in mutualistic networks. PhD in Biosciences (NERC GW4 + DTP) Ref: 3656

About the award


Lead Supervisor 

Dr Christopher Kaiser-Bunbury, Department of Biosciences, College of Life and Environment SceincesUniversity of Exeter 

Additional Supervisors

Dr Richard James, Department of Physics, University of Bath

Dr Benno Simmons, Department of Biosciences, College of Life and Environmental Sceinces, University of Exeter

Location: University of Exeter, Penryn Campus, Penryn, Cornwall, TR10 9FE

This project is one of a number that are in competition for funding from the NERC GW4+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the GW4 Alliance of research-intensive universities: the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five unique and prestigious Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in the Earth, Environmental and Life sciences, designed to train tomorrow’s leaders in scientific research, business, technology and policy-making. For further details about the programme please see

For eligible successful applicants, the studentships comprises:

  • An stipend for 3.5 years (currently £15,009 p.a. for 2019/20) in line with UK Research and Innovation rates
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £3,250 for specialist training courses and expenses.
  • Travel and accommodation is covered for all compulsory DTP cohort events
  • No course fees for courses fun by the DTP

We are currently advertising projects for a total of 10 studentships at the University of Exeter


Students who are resident in EU countries are eligible for the full award on the same basis as UK residents.  Applicants resident outside of the EU (classed as International for tuition fee purposes) are not eligible for DTP funding. Residency rules are complex and if you have not been resident in the UK or EU for the 3 years prior to the start of the studentship, please apply and we will check eligibility upon shortlisting.

Project Background

Mutualistic interactions between plants and animals form complex ecological networks. Such networks are essential for maintaining biodiversity, which provides many important services to humanity, such as water purification, waste decomposition and crop pollination. To understand how communities will respond to environmental change, it is essential to understand how network structure relates to population dynamics operating on networks. However, research examining this structure-dynamics relationship has focused exclusively on the structure of the network as a whole (the ‘global’ structure). The question remains to what extent smaller-scale structures in networks (the ‘local’ scale) are relevant to infer the dynamics of entire mutualistic networks. Results from this question will be used to inform the conservation and restoration of pollinator ecosystems.

Project Aims and Methods

With this project we aim to understand how local network structure shapes global dynamics in mutualistic (e.g. pollination or seed dispersal) networks. By examining the local scale, we expect to gain a detailed understanding of the topological basis of dynamics on networks. We will use mathematical models to simulate population dynamics on networks and quantify desirable dynamic characteristics, such as stability, persistence and resilience to critical transitions. These characteristics will then be related to the local structure of networks, quantified using motifs (small subnetworks comprising interactions between a small number of nodes; Simmons et al. 2019a)

Specific aims of the project include:

1. Use dynamical simulations and motif analysis to understand how local structure influences whole-network dynamics.

2. To identify local regions of networks that have a disproportionate influence on whole-network dynamics through time. To do this, we will consider dynamic processes operating on networks that have changing structure over time. 

3. To quantify differences in local network structure, it is necessary to develop tools for comparing network topology, as current approaches in ecology are very crude. This part of the project will focus on developing an R package that calculates more sensitive metrics of structural difference between networks, such as graph edit distance, largest common subgraph and smallest common supergraph.

To produce results of real practical use, e.g. the conservation/restoration of plant-pollinator interactions, we will use world-class datasets of networks over time from the Seychelles (Kaiser-Bunbury et al. 2010, 2014, 2017, and publ. data 2018-2020) and characterise local regions of networks that remain dynamically important even in the presence of changing whole-network structure. 



Fig. 1. Pollination networks collected on Seychelles ‘inselbergs’. Small example network, showing interactions between different plant and pollinator species. Carpenter bees visiting exotic plant in Seychelles. Graph showing modelled species population dynamics over time; similar outputs will be obtained in this project (from top left to bottom right)


Fig. 2. (a) Three bipartite networks with identical values of three whole-network indices. (b) Motifs reveal very different local structure (adapted from Simmons et al. 2019).



Fig.3. Long-term highly-resolved temporal data sets of pollination networks are rare. You will be working with world-class empirical networks over time from Seychelles (shown here Kaiser-Bunbury et al. 2017) to test modelling-derived predictions of dynamic processes on the local network scale.

Candidate Requirements

This project would suit an ecologist with mathematical and computational skills, or a mathematician, physicist or computer scientist with an interest in ecology.


You will work alongside a diverse team of tropical network ecologists in the research group, which will open up extensive training opportunities ranging from 1) project-specific training, e.g., in R (the primary programming language used in ecology), motif methods for characterising local network structure, and methods for simulating dynamics on networks; to 2) general ecological and conservation training, e.g., in pollination and applied network ecology, island evolution, natural history, and population and community ecology. Embedded in a vibrant community of world-class ecologists and conservation scientists at CEC, you will be able to obtain the full range of skills in the ecologist’s toolbox, including training in science communication, peer-review, project management, and translating your work into impact. 

References / Background reading list 

Kaiser-Bunbury, CN, et al. (2017). Nature, 542, 223–227; Kaiser-Bunbury, CN, et al.  (2014). Ecology, 95, 3314-3324; Kaiser-Bunbury, CN, et al.  (2010). Ecol. Lett., 13, 442–452; Kondoh, M, (2008). PNAS, 105, 16631-35; Simmons, BI, et al. (2019a). Oikos, 128, 154–170; Simmons, BI & Hoeppke, C (2018). J. Anim. Ecol., in press, DOI; Simmons, BI, et al. (2019b). Meth. Ecol. Evol., in press. DOI; Simmons, BI, et al. (2019c). Oikos, 128: 1287-1295; Stouffer, DB & Bascompte, J (2010). Ecol. Lett., 13, 154-161.

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

All applicants would need to meet our English language requirements by the start of the  project


How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • CV
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English.

Reference information
You will be asked to name 2 referees as part of the application process, however we will not expect receipt of references until after the shortlisting stage. Your referees should not be from the prospective supervisory team.

If you are shortlisted for interview, please ensure that your two academic referees email their references to the, 7 days prior to the interview dates.  Please note that we will not be contacting referees to request references, you must arrange for them to be submitted to us by the deadline.

References should be submitted by your referees to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is 1600 hours GMT Monday 6 January 2020.  Interviews will be held between 10 and 21 February 2020.  For more information about the NERC GW4+ DPT please visit

If you have any general enquiries about the application process please email  Project-specific queries should be directed to the lead supervisor.

Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  • the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
  • administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.


Application deadline:6th January 2020
Value:£15,009 per annum for 2019-20
Duration of award:per year
Contact: PGR Enquiries