ELIGIBILITY: This project is for a student taking the MSc in Statistics and Sustainability. Interested students should contact Gabriel Palma at GPALMA@tcd.ie
Insect outbreaks have frequently been documented in insect pest populations in various agroecosystems presenting serious economic damage. To address this problem, Integrated Pest Management (IPM) present various techniques to reduce the insect population involving a combination of biological, Chemical, physical and other types of interventions. One important question for IPM involves the selection of an appropriate time for these interventions.
Another important aspect of these questions is the uncertainty assessment, to provide additional information to IPM decision makers [1]. Therefore, this project will focus on exploring different formulation of Bayesian Hierarchical Models to forecast insect abundances.
A familiarity with R programming language and Bayesian methods is expected from the student. Please email gpalma@tcd.ie if you are interested in this project
References
[1] Gabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy, Eduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral, Forecasting insect abundance using time series embedding and machine learning, Ecological Informatics, Volume 85, 2025, 102934, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2024.102934. (https://www.sciencedirect.com/science/article/pii/S157495412400476X)
[1] Gabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy, Eduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral, Forecasting insect abundance using time series embedding and machine learning, Ecological Informatics, Volume 85, 2025, 102934, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2024.102934. (https://www.sciencedirect.com/science/article/pii/S157495412400476X)