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PhD project - Estimating spatio-temporal trends of plant species using Opportunistic Citizen science data
This project is my PhD thesis work at Université de Montpellier (France), in Inria/LIRMM team. It started in October 2024 under the supervision of Alexis Joly, Maximilien Servajean, and Christophe Botella.
The current directions are described below.
I'm open to suggestions and collaborations, feel free to contact me!
Summary
Monitoring species distributions is critical for understanding ecological dynamics and informing conservation efforts, especially in the context of current global changes. Although opportunistic data from citizen science programs offer extensive spatial and temporal coverage, they are affected by substantial sampling and detection biases. This PhD thesis proposes a method for estimating species distributions and their temporal trends from presence-only data using hierarchical site-occupancy models that account for both types of bias. We explore the use of deep learning to fit these models efficiently and flexibly. We plan to apply this framework to real-world datasets, such as Pl@ntNet, and further investigate model extensions that incorporate more complex observer behaviors and detection processes using various neural network architectures. We will use these models to assess trends in plant species in terms of coverage, population changes and range shifts. Furthermore, one of the objectives of this PhD is to study the factors that influence these trends such as land use changes, climate change, and pollution.
