Harmonizing Plant Growth Models with Ecosystem Processes for Ecological Resilience

Abstract

Environmental sustainability necessitates a comprehensive understanding of how biological and ecological processes interact across various scales. Plant growth models provide insights into physiological and biochemical processes at the organism level, while ecosystem dynamics models capture community interactions, energy flows, and biogeochemical cycles. Despite their significance, these two domains are often studied independently, which limits their applicability in addressing complex environmental challenges.

This paper explores the integration of plant growth models with ecosystem dynamics to create robust frameworks for sustainable environmental management. Such integration allows for better predictions of carbon sequestration, nutrient cycling, water use efficiency, and biodiversity resilience under varying climatic and anthropogenic conditions.

The study emphasizes the methodological approaches for coupling models, including system dynamics, process-based simulations, and data-driven machine learning techniques. Furthermore, it highlights real-world applications such as climate change mitigation, precision agriculture, and ecosystem restoration. Challenges in data harmonization, model calibration, and computational complexity are discussed, alongside opportunities for leveraging remote sensing, artificial intelligence, and big data analytics. By bridging organismal and ecosystem scales, integrated models present a powerful tool for guiding policy, supporting adaptive management, and ensuring long-term environmental sustainability.