Any network or web-like structure consists of "actors" that are linked by some type of activity. Thus, inference for features in a friendship network, protein-protein interaction network, or a predator-prey food web can be made using a common statistical framework as a foundation. In the context of food webs, our Bayesian methodology (DOI: 10.1073/pnas.1015359108; DOI: 10.1016/j.stamet.2013.09.001; DOI: 10.13140/RG.2.2.28617.54887) extends upon latent space network modelling to address the notion of "trophic levels" from three perspectives of feeding behaviour: (1) activity level as predator and prey, (2) feeding preference as predator, and (3) feeding preference as prey. Worked examples and model implementation will be discussed.

Dr Grace Chiu's research focuses on integrative, holistic statistical methodologies that tackle multi-faceted environmental and biological problems. Graduate students and interns under her supervision from the past and present have conducted statistical research with an environmental focus. Further information can be found on her ANU Researcher Profile Page.