Learning the syntax of plant assemblages.

Area of Science:

• Ecology
• Computational Biology
• Artificial Intelligence

Background:

• Understanding plant species distribution is vital for addressing the biodiversity crisis.
• Predicting species composition and habitat type is crucial for conservation and restoration but remains challenging.
• Traditional methods struggle to capture complex species interactions and micro-environmental influences.

Purpose of the Study:

• To develop an AI-driven methodology for analyzing plant species assemblages.
• To learn the 'syntax' of species sequences and capture latent associations between species.
• To improve predictions of species composition and habitat types for ecological applications.

Main Methods:

• An approach inspired by large language models (LLMs) was adapted to analyze abundance-ordered plant species sequences.
• The model learns latent associations between species across diverse ecosystems.
• The methodology was fine-tuned for predicting missing species and classifying habitat types.

Main Results:

• The proposed method demonstrated higher accuracy in predicting missing species (16.53% over co-occurrence matrices, 6.56% over neural networks).
• It achieved superior accuracy in habitat type classification (5.54% over expert systems, 1.14% over tabular deep learning).
• The application's vocabulary covers over 10,000 plant species from Europe and adjacent regions.

Conclusions:

• This AI-powered approach offers a powerful new tool for biodiversity mapping, restoration, and conservation biology.
• It provides a novel way to model, monitor, and understand ecological systems.
• The methodology opens new avenues for ecologists integrating artificial intelligence into ecological research.