Knowledge-Guided Machine Learning for Global Change Ecology Research

Area of Science:

• Ecology
• Artificial Intelligence
• Computational Science

Background:

• Global change ecology requires predictive models that combine data-driven learning with mechanistic theory.
• Traditional models face challenges in spatiotemporal parameterization (process-based) or generalization and interpretability (data-driven).
• Existing approaches struggle to address complex, interconnected ecosystem challenges effectively.

Purpose of the Study:

• To review the transformative potential of Knowledge-Guided Machine Learning (KGML) in global change ecology.
• To showcase KGML's capacity to improve predictions of crucial ecological processes like carbon-water-nutrient cycles.
• To explore KGML's role in developing ecological foundation models and deriving actionable insights.

Main Methods:

• Systematic integration of ecological principles (e.g., physical laws, stoichiometry, process understanding) into machine learning models.
• Designing, training, and adjusting models to ensure generalization across diverse ecosystems.
• Reviewing emerging applications in decision support and symbolic regression.

Main Results:

• KGML offers a robust framework to bridge the gap between data-driven and theory-driven modeling approaches.
• Demonstrates enhanced prediction capabilities for carbon-water-nutrient cycles and other ecological processes.
• Highlights potential for developing ecological foundation models and generating novel hypotheses.

Conclusions:

• KGML represents a significant advancement for global change ecology, uniting ecological theory with AI.
• Future directions include adaptive data-knowledge integration, uncertainty quantification, and causal embedding.
• KGML is crucial for fostering scientific discovery and developing sustainable solutions for ecosystem challenges.