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

Lead Supervisor
Dr Christopher Kaiser-Bunbury, Department of Biosciences, College of Life and Environment Sciences, University of Exeter

Additional Supervisors:
- Dr Richard James, Department of Physics, University of Bath
- Dr Benno Simmons, Department of Biosciences, College of Life and Environment Sciences, 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 http://nercgw4plus.ac.uk/

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


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.