Methodological Insights into Implementing cellular automata models for simulating seagrass dynamics: Responses to...

Area of Science:

• Ecology
• Environmental Science
• Computational Modeling

Background:

• Seagrass ecosystems are vital marine habitats facing significant threats from global environmental changes.
• Predictive modeling is crucial for understanding and managing seagrass dynamics under these pressures.

Purpose of the Study:

• To introduce and demonstrate a procedural framework for developing Cellular Automata (CA) models for seagrass ecosystem simulation.
• To showcase the adaptability of CA models for diverse geographical locations and ecological conditions.

Main Methods:

• Development of a comprehensive CA modeling methodology, including conceptualization, workflow design, parameterization, and execution.
• Application of the CA model to seagrass ecosystems in the Mediterranean and East Africa (Zanzibar) as case studies.

Main Results:

• The CA models successfully simulated seagrass dynamics, capturing the effects of climate change, invasive species, and nutrient variations.
• Demonstrated the versatility of the CA approach across different geographic regions, species compositions, and model complexities.

Conclusions:

• Cellular Automata models offer a robust framework for simulating seagrass ecosystem responses to environmental changes.
• Future research should focus on addressing parameterization complexity and model validation to enhance accuracy and applicability.