Rate My Professor Michael Renton

Michael Renton is an Associate Professor in the School of Biological Sciences within the Faculty of Science at the University of Western Australia. He earned his PhD in computational biology from the University of Queensland between 2000 and 2004, with a thesis titled 'Function, form and frangipanis: Modelling the patterns of plant growth' conducted at the Centre for Plant Architecture. Previously, he completed an Honours degree in Applied Mathematics at the University of Western Australia, focusing on control in chaotic systems. Renton holds a Graduate Diploma in Secondary Education from Edith Cowan University and worked as an English Teacher at UWA in 1999.

Renton's research centres on ecological and evolutionary modelling applied to agricultural and natural systems. Key areas include the evolution of resistance to herbicides and pesticides, crop protection from weeds, pests, and pathogens, biosecurity and dispersal of invasive organisms, conservation and restoration, optimal land use in cropping and mixed landscapes, and theoretical ecology. His expertise encompasses functional-structural plant modelling, individual-based modelling, statistical modelling, and analysis of complex biological systems. He leads research projects, supervises postgraduate students, and teaches courses in plant biology, ecology, and ecological modelling and analysis. Notable publications include 'Experience teaches plants to learn faster and forget slower in environments where it matters' (Gagliano et al., Oecologia, 2014), 'Out of sight but not out of mind: Alternative means of communication in plants' (Gagliano et al., PLOS ONE, 2012), 'High level of resistance to Sclerotinia sclerotiorum in Brassica rapa' (Garg et al., 2010), and 'Does cutting herbicide rates threaten the sustainability of weed management in cropping systems?' (Renton et al., Journal of Theoretical Biology, 2011). His work has amassed over 8,000 citations, influencing sustainable weed management, plant behaviour studies, and ecological simulation models.