Supervisor: Dr Athen Ma
Research group(s): Cognitive Science, Networks
Climate change is projected to increase the likelihood of extreme environmental conditions, causing major transformation of ecosystems. These include the loss of species and altered biomass fluxes through the food web. The patterning and strength of species interactions within the food web can play a huge role in determining how the system responds to environmental perturbations, but surprisingly, many food web metrics appear to be insensitive to these major perturbations. For instance, drought is said to triggered partial collapse of food webs in which both species and links are loss and yet, macroscopic network measures, such as connectance and interaction diversity, were unaltered by this disturbance.
The project aims to develop a more functional understanding of how higher-level (e.g. food web) properties respond to environmental conditions and stressors. By coupling techniques in network science with ecological theories, we assess a novel aspect of functional biodiversity (species interactions) that has been overlooked in biomonitoring, due to the bias towards focusing on the nodes (i.e. species) within food webs rather than the links (e.g. trophic interactions between species pairs). Network properties, such as nestedness and modularity, have been linked to propagation of disturbance in food webs and impact on network stability, and we are still at an embryonic stage in understanding the functional role of different network structures and properties and their ecological implications. In particular, current assessment on network robustness relies heavily on simulated removal of a large proportion of species, and this approach is, somewhat, unrealistic and it is not effective in capture immediate and short-term impacts of the extinction of a given species. By employing network theory and dynamical modelling, the project aims to gain a better understanding on the effect of perturbations in food webs, so as to advance the predication of stability in complex natural systems and reveal the key underlying organisational properties that contribute towards stability.