Presenters-0685
From CSDMS
CSDMS 2025: Exploring Earth's Surface with Models, Data & AI
Advancing Ecological Modeling with pyMANGA: Modularity and Reusability for Robust and Reproducible Research
Abstract
Individual-based vegetation models are essential for understanding and predicting ecosystem responses to environmental change. While these models rely on well-established process descriptions - such as vegetation establishment, growth and mortality - they are often developed from scratch, leading to inefficiencies. We present pyMANGA, an open-source, modular platform designed to streamline model development and enable systematic hypothesis testing. By allowing researchers to combine, modify and extend different concepts of plant growth, competition and resource dynamics, pyMANGA supports flexible, reproducible modelling. The platform is particularly suited to the study of ecohydrological interactions, including soil-plant feedback loops in coastal ecosystems. Transparency is ensured through open-source access, version control and automated benchmarking, while a structured review process fosters collaboration. Defined interfaces make it easy to compare models of varying complexity and abstraction, improving reproducibility and robustness. By providing an efficient and extensible framework, pyMANGA advances ecological modelling and improves decision making in environmental science.
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