The Rapid Initial Community Builder (RICB) V1.1 for LANDIS-II.

Area of Science:

• Forest Ecology
• Computational Modeling
• Land Management

Background:

• Ecosystem process models, particularly landscape class models, are crucial for predicting forest resilience and future structure under disturbance.
• Current models require extensive user input for site-specific initialization, limiting widespread adoption.
• Generating initial forest communities for these models is a significant bottleneck.

Purpose of the Study:

• To develop a tool that automates and streamlines the creation of initial forest communities for landscape ecosystem process models.
• To reduce the time and technical barriers associated with model setup for land management applications.

Main Methods:

• The Rapid Initial Community Builder (RICB) was developed as an executable file.
• RICB utilizes high-resolution CONtinental United States (CONUS) forest coverage and type data.
• Data is modified for compatibility with the LANDIS-II ecosystem process model, removing coding barriers.

Main Results:

• RICB significantly reduces the time required for generating initial forest communities.
• The tool eliminates the need for users to possess coding expertise or manage software versioning.
• Implementation of landscape ecosystem process models for new sites is greatly facilitated.

Conclusions:

• The Rapid Initial Community Builder (RICB) effectively addresses the challenge of intensive data assimilation for model initialization.
• RICB enhances the accessibility and practical application of landscape ecosystem process models in land management.
• This automation supports more efficient and widespread use of forest simulation tools for decision-making.