Hi! I’m Will, and I’m undertaking a PhD at the University of Leeds. I’m based at the National Centre for Atmospheric Science, and work alongside my supervisor, Dr Ryan Neely III, and a team of researchers spanning radar meteorology, ecology, genetics, and data science as part of the BioDAR Project. Together, we develop and inform machine-learning algorithms to allow weather surveillance radars to locate, identify, and quantify airborne insect communities.
I’m an interdisciplinary conservation scientist with a passion for research that exploits traditional field skills and remote-sensing technologies to understand and combat insect declines. I completed an undergraduate degree in Zoology at the University of Sheffield and graduated with an MSc in Biodiversity & Conservation from the University of Leeds. My Masters thesis explored the relationships between morphological and dual-polarisation radar-derived observations of nocturnal macro-moth communities.
In my spare time I enjoy making soup, spending quality time with my tortoise, Fajita, and organising field trips under the guise of weekend getaways with my partner.
2018-2020 – MSc Biodiversity & Conservation, University of Leeds
2007 – 2010: BSc Zoology, University of Sheffield
My research interests include radar meteorology; remote-sensing; insect conservation, biogeography, and physiology; quantitative and community ecology.
My teaching interests include experimental design; data analysis; insect identification; habitat management; community ecology
BioDAR: Using Weather Radars and Machine-Learning to Examine Insectageddon
Dr Ryan Neely III, Dr Christopher Hassall, Professor Bill Kunin, Dr Elizabeth Duncan, Dr Maryna Lukach
Panorama NERC DTP, 2021
In the first phase of my PhD I will use hi-res 3D scanning technology and electromagnetic modelling software to simulate the dual-polarisation radar characteristics of airborne insect communities. In the second phase, I will compare the results of these simulations with empirical observations of insect communities gathered via a) Dual-polarisation weather radar, b) Vertical-looking radar, and c) High-altitude samples gathered via Helikite.
In the third phase these simulations, radar scans, and high-altitude samples will be used to define and validate relationships between ecological and weather radar-derived observations of insect communities. These relationships will then be integrated into machine-learning algorithms for weather radars, giving the ability to monitor insect populations and their stressors at low cost, high frequency, and over wide spatial scales (>30km).