Towards globally customizable ecosystem service models.

Area of Science:

• Ecosystem Services (ES) modeling
• Artificial Intelligence (AI) applications in environmental science
• Spatial analysis and prioritization

Background:

• Modeling ecosystem services (ES) involves a trade-off between simple, rapid methods and complex, data-intensive approaches.
• Complex models offer accuracy and local specificity but are challenging to implement and scale.
• Simple models are faster but may lack accuracy and credibility.

Purpose of the Study:

• To introduce the ARtificial Intelligence for Ecosystem Services (ARIES) platform's "Tier 1" models.
• To provide accessible ES models ranging from simple to complex, adaptable to user needs.
• To demonstrate rapid ES quantification and spatial prioritization using customizable models.

Main Methods:

• Developed five "Tier 1" ES models runnable globally with default data.
• Enabled user customization of models with context-specific data and parameters.
• Utilized ARIES' spatial multi-criteria analysis module for ES prioritization.

Main Results:

• Demonstrated rapid ES quantification and customized assessments at three global locations.
• Showcased the adaptability of models using publicly available and high-resolution data.
• Facilitated spatial prioritization of ES for various beneficiary groups.

Conclusions:

• The ARIES platform effectively bridges the gap between simple and complex ES modeling.
• User engagement and community contributions enhance the platform's knowledge base and capabilities.
• ARIES supports scalable, accurate, and accessible ES assessments for global decision-making.