L'intelligence artificielle au service de la modélisation de la biodiversité : de la prévision des assemblages d'espèces végétales à la compréhension et à la cartographie des habitats
Bibliographic record
Abstract
Biodiversity is undergoing rapid change due to global environmental pressures, yet our ability to monitor and predict species distributions and ecosystem dynamics remains limited by the quality and scale of available data and models. Hence, in this doctoral thesis, we develop and evaluate several artificial intelligence methods, ranging from convolutional neural networks to large language models, to better map and understand European vascular plant species, essential biodiversity indicators, and terrestrial habitat types. Firstly, we assemble an extensive dataset that integrates millions of vascular plant species observations (both from citizen science and scientific experts data) combined with various environmental predictors at high-resolution (such as satellite images, climatic time series, and rasterized environmental variables). Building on this, we design a multimodal ensemble model to accurately predict species occurrences and derive biodiversity indicators for ecosystem health monitoring. We then focus on the core of the project: identification of habitats based on species assemblages, using deep learning and emphasizing interpretability to uncover ecological drivers. To capture the latent structure of plant communities, we introduce a novel use of large language models, fine-tuned to learn the syntax of co-occurring plants and thus able to detect missing species from incomplete surveys. Finally, we combine all these steps in a cascading pipeline capable of mapping species distributions, biodiversity indicators, and habitat types at high resolution across Europe, enabling previously infeasible large-scale ecological assessments by offering new tools for biodiversity monitoring, conservation planning, and land-use management. This work demonstrates the potential of modern machine learning approaches to address longstanding challenges in ecology. Using remote sensing, environmental variables, and species occurrence data, we built a pipeline offering a comprehensive view on ecosystems dynamics. By making all discoveries open source, we provide a scalable framework for future biodiversity assessment and conservation planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".