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Multimodal Graphs for Biodiversity Intactness Understading

  • Funding: DLA AI-Intervene, self-funding
  • Project Title: Multimodal Graphs for Biodiversity Intactness Index
  • Main Supervisor: Alan Guedes
  • Collaboration: National History Museum

About

The escalating impact of human activities on biodiversity, intensified by climate change, and quantifiable by the Biodiversity Intactness Index (BII), demands new prediction-based methods. Current BII prediction methods primarily stem from an ecological perspective, often lacking the integration of recent advancements in machine learning. While a wealth of multimodal biodiversity data exists, from reports to environmental data (climate and land usage), these valuable sources remain isolated, preventing a holistic understanding of intricate ecological relationships. MM-BioGraph directly addresses BII by the integrating tinto graph-based structures and using existing knowledge bases (e.g. NHM Planetary Knowledge Base). This novel approach represents relationships among species and environmental variables, revealing patterns and uncovering hidden connections. This project is a strategic partnership with the Natural History Museum's (NHM) Future Lab, leveraging their expertise on their BII dataset called PREDICTS. Central to our methodology is the innovative application of Graph Neural Networks (GNNs) and Large Language Models (LLMs). This approach will not only overcome the limitations of current ecology regression models but also provide graph-based explainability support. We will build predictive models capable of understanding these relationships in the context of BII prediction, identifying previously unknown ecological connections obscured by biodiversity data complexity. Therefore, this project is set to deliver highly impactful new methods for biodiversity conservation.

Our primary goals are to: 1) Establish a comprehensive multimodal biodiversity dataset encompassing knowedge graphs, ecological conversation, and land usage data to ensure robust graph representation, leveraging the PREDICTS dataset, followed by a GNN-based baseline model to identify species interactions and ecosystem dependencies. 2) Advance multimodal modeling with LLMs by developing cutting-edge models that integrate LLMs with visual data, surpassing the GNN baseline to predict complex relationships, delivering more accurate and comprehensive insights.

References

  • https://www.nhm.ac.uk/our-science/services/data/biodiversity-intactness-index.html
  • https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579
  • https://dl.acm.org/doi/abs/10.1145/3581783.3612266
  • https://arxiv.org/abs/2403.07311
  • https://biss.pensoft.net/article/111168

Datasets:

  • https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database