Vegetation dynamics: Modeling, mechanisms, and emergent properties

Abstract

As a core component of terrestrial ecosystems, vegetation plays an irreplaceable role in regulating climate and maintaining ecological balance. However, vegetation dynamics often exhibit strong spatial heterogeneity and nonlinear responses, necessitating the development of an integrated modeling and analysis framework to reveal their underlying mechanisms and guide restoration efforts. This review systematically summarizes recent advances and key methodologies in vegetation dynamics research, focusing on four major dimensions: modeling mechanisms, nonlinear behaviors, ecosystem resilience assessment, and restoration pathway optimization. We first examine reaction–diffusion models based on representative ecological mechanisms such as scale-dependent feedbacks, motility-induced phase separation, and belowground interactions, and introduce stochastic and data-driven models to better capture the uncertainty and multi-source complexity inherent in natural systems. The review also explores nonlinear phenomena such as multistability, regime shifts, and localized structures, employing bifurcation analysis and amplitude equations to investigate pattern selection and system stability. We further review a range of early warning signal indicators based on critical slowing down, spatial patterns, and entropy, and introduce machine learning approaches to enhance predictive capability. Furthermore, we comprehensively review various optimal intervention methods including terminal control, boundary control, and sparse control. Finally, we discuss current challenges and future opportunities in theoretical integration, practical implementation, and cross-scale coordination. This review aims to provide systematic theoretical support and practical guidance for ecological modeling, restoration engineering, and global environmental governance.