Exploring and Predicting Plant-Arthropod Interactions: Hierarchical Modelling of Species Communities and Graph Neural Network Approaches

dc.contributor.advisorTaylor, Graham
dc.contributor.authorEl-Shawa, Sara
dc.date.accessioned2023-05-18T16:47:04Z
dc.date.available2023-05-18T16:47:04Z
dc.date.copyright2023-05
dc.date.created2023-05-15
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen
dc.degree.nameMaster of Applied Scienceen_US
dc.degree.programmeEngineeringen_US
dc.description.abstractThe world's biodiversity is encountering unprecedented threats, with more than one million plant and animal species at risk of extinction as of 2022. In this thesis, we explore the interactions between plants and arthropods, beginning with an investigation of environmental influences on arthropods using joint species distribution modelling. Our results demonstrate that incorporating specific plants significantly improves model performance, highlighting the importance of considering plant diversity when studying arthropod communities. We then focus on predicting plant-arthropod interactions using Graph Neural Networks (GNN) adapted to our dyadic data. Our proposed approach encompasses different model architectures and choices of arthropod features. We find that GNN-based systems achieve moderate performance, which could potentially be improved by integrating higher-quality data and features. Together, the insights gained from the distributional modelling of species communities and GNN models of dyadic data provide a deeper understanding of plant-arthropod interactions and their complex interplay with diverse factors. This research contributes to the broader knowledge of biodiversity and ecosystem functioning, with potential applications in ecological research and conservation efforts.en_US
dc.identifier.urihttps://hdl.handle.net/10214/27633
dc.language.isoenen_US
dc.publisherUniversity of Guelphen
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectSpecies distribution modellingen_US
dc.subjectArtificial intelligenceen_US
dc.subjectGraph neural networksen_US
dc.subjectPlant-arthropod interactionsen_US
dc.subjectMachine learningen_US
dc.subjectBiodiversity lossen_US
dc.titleExploring and Predicting Plant-Arthropod Interactions: Hierarchical Modelling of Species Communities and Graph Neural Network Approachesen_US
dc.typeThesisen

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