Knowledge‐Guided Machine Learning for Global Change Ecology Research - Zhenong Jin; Licheng Liu; Qi Yang; Xiaowei Jia; Shengli Tao; Yinkun Guo; Rahul Ghosh; Sheng Wang; Qing Zhu; Martin Jung; Kaiyu Guan; Vipin Kumar; et al. (Global Change Biology, 2026)
Global change ecology demands predictive models that reconcile data‐driven learning with mechanistic theory to address complex, interconnected ecosystem challenges. Traditional process‐based approaches struggle with spatiotemporal parameterization, while purely data‐driven machine learning approaches suffer from extrapolation, interpretability, and physical consistency. Knowledge‐guided machine learning (KGML) bridges this divide by systematically integrating ecological principles (e.g., physical first principles, stoichiometry, process understanding, disturbance regimes) into how models are designed, trained, and adjusted to generalize across different ecosystems. The emerging KGML paradigm offers tremendous opportunities to advance the research of global change ecology. This review synthesizes KGML's transformative potential, showcasing its capacity to enhance the prediction of carbon‐water‐nutrient cycles and other ecological processes and lay groundwork for ecological foundation models. Emerging applications in decision support and symbolic regression further illustrate its role in deriving actionable insights and novel theoretical hypotheses. Future directions emphasize adaptive integration of data and knowledge, uncertainty quantification, causal embedding in foundation models, and interdisciplinary collaboration to align KGML innovations with sustainability goals. By uniting ecological theory with AI advances, KGML offers a robust pathway to encompass ecosystem responses to global change, fostering scientific discovery and actionable solutions.