The preservation of biodiversity is critical for maintaining ecological balance and ensuring the sustainability of ecosystems. However, biodiversity faces numerous threats, including habitat loss, climate change, and the proliferation of invasive species. Addressing these challenges requires comprehensive monitoring, predictive and conservation planning capabilities that currently do not exist [1].
Deep learning Foundation Models (FMs) [2] have revolutionized numerous scientific domains by leveraging vast datasets to learn general-purpose representations adaptable to various downstream tasks. This paradigm holds immense promise for biodiversity conservation.
In this talk, we introduce the concept of Biodiversity Foundation Model (BFM), a large-scale, multimodal AI model pre-trained on diverse biodiversity data modalities. These modalities include imagery, audio recordings, genomic data, environmental DNA (eDNA), satellite and remote sensing data, geospatial data, climate data, textual data, and sensor data. The BFM aims to enhance biodiversity monitoring, prediction, and conservation efforts, while being flexible and robust to any kind of downstream task, from classification to prediction.
Drawing parallels from models like Aurora [3] and Prov-GigaPath [4], we hypothesize that the BFM can significantly outperform traditional methods in biodiversity-related tasks. For example, using pre-trained weights from a vast dataset of environmental DNA, the BFM could rapidly identify and monitor species presence in various habitats, providing critical data for conservation efforts.
The BFM can transform biodiversity conservation in several ways:
- Enhanced Monitoring: By integrating diverse data sources, the BFM provides comprehensive and real-time monitoring of ecosystems.
- Predictive Analytics: BFM predict future changes in biodiversity due to various factors, enabling proactive conservation measures.
- Invasive Species Management: Early detection and monitoring of invasive species through BFM can help mitigate their impact on native ecosystems.
- Climate Change Adaptation: The BFM can identify potential climate refugia and assist in developing strategies to protect vulnerable species.
Still, like any other advance AI model, BFM comes with a series of challenges from diverse and vast amount of data download, storage and pre-processing, to architecture development, training, test, evaluation and finally safe deployment. Each of these topics require careful handling and a multi-disciplinary team with ecologist, computer scientists, AI and HPC experts to ensure its success.
The session will feature both presentations and an interactive open discussion segment. We warmly invite you to participate in this engaging dialogue, where we will collectively delve into the significant advancements and key challenges associated with the development and utilisation of Foundation Models in the dynamic field of Earth Sciences.