A synergistic future for AI and ecology

Area of Science:

• Ecology and Artificial Intelligence (AI)
• Complex Systems Science
• Global Change Research

Background:

• Ecology and AI have advanced independently, yet both seek to understand complex systems with nonlinear interactions.
• Global change necessitates a synergistic approach to address challenges like unpredictability and resilience dynamics.
• Ecological systems present modeling challenges, even for advanced AI techniques like deep neural networks.

Purpose of the Study:

• To highlight the urgent need for a convergence research paradigm between ecology and AI.
• To explore how AI can advance ecological modeling and how ecological systems can inspire AI.
• To emphasize the mutual benefits of synergy for understanding ecological resilience and improving AI robustness.

Main Methods:

• Conceptual review and synthesis of research trends in ecology and AI.
• Identification of shared challenges and opportunities for interdisciplinary collaboration.
• Case examples illustrating the potential of AI in ecological systems modeling.

Main Results:

• Ecological systems' emergent behaviors can inspire novel AI architectures.
• AI advancements offer powerful tools for modeling complex ecological dynamics.
• Synergy can accelerate understanding of ecological resilience and improve AI generalization.

Conclusions:

• A purposeful convergence between ecology and AI is critical for addressing global change.
• This interdisciplinary synergy can lead to breakthroughs in both fields and enhance future adaptability.
• Overcoming epistemic barriers is essential for realizing the full potential of this collaboration.