Combinatorial Maps, a New Framework to Model Agroforestry Systems

Area of Science:

• Agroecology
• Systems Ecology
• Computational Ecology

Background:

• Agroforestry systems exhibit complex, long-term interactions between structural and functional components.
• Current numerical models often isolate either structure or function, failing to capture their interdependence.

Purpose of the Study:

• To develop a unified framework for representing agroforestry systems, integrating structure and function.
• To conceptualize the structure-function relationship at the agroecosystem scale using a novel modeling approach.

Main Methods:

• Utilized combinatorial maps, a type of multidimensional graph, for system representation.
• Developed a Python-based implementation of the framework, available on GitHub.
• Demonstrated the model's capability to represent multi-scale structure and temporal evolution.

Main Results:

• The combinatorial map framework successfully models the structure and dynamics of agroforestry systems.
• The approach allows for a multi-scale and temporal representation of agroecosystem complexity.
• The Python implementation provides a practical tool for agroforestry research.

Conclusions:

• Combinatorial maps offer a unifying and generic approach to describing agroforestry systems.
• This framework can be integrated with other models for predicting ecosystem services.
• The model facilitates translation between different representational methods for agroecosystems.