Modeling plant-environment interactions with integrated machine learning and optimality theory

Understanding vegetation dynamics is essential for predicting water, carbon, and energy exchanges in terrestrial ecosystems. Despite advances in plant-environment interaction models, challenges remain in accurately representing how key plant traits, such as roots, respond to environmental variability, particularly in arid ecosystems. Current models often rely on fixed mathematical representations, limiting their ability to address complex and dynamic plant-environment interactions. For instance, optimality-based vegetation models, which use long-term carbon profit optimization principles, show promise but are typically still constrained by predefined functional forms.

This work presents a conceptual framework that attempts to integrate machine learning with optimality-based vegetation modeling, aiming to combine the strengths of mechanistic modeling and data-driven approaches. This framework is designed to capture diverse plant-environment processes, such as root development, over various temporal scales. Within this hybrid framework,  plants in simulated environments are enabled to dynamically adjust their responses based on optimization objectives. Preliminary simulations with the FLUXNET datasets suggest that the framework has the potential to better predict ecosystem fluxes and improve our understanding of vegetation dynamics under changing conditions.

This study highlights the potential of integrating machine learning with plant physiological processes to address current limitations in modeling plant-environment interactions. The proposed framework could serve as a flexible tool for exploring vegetation dynamics and their implications for ecosystem function.