Beyond seasonality: A data-fusion approach reveals extreme climate and qualitative ecosystem traits as key litterfall drivers across diverse forests

Accurate prediction of monthly forest litterfall is vital for understanding ecosystem carbon and nutrient cycling. However, this process is governed by complex drivers that vary substantially across different forest ecosystems, posing significant challenges for predictive modeling, especially at high temporal resolutions. Existing models often overlook the combined influence of extreme climatic events and nuanced, qualitative site-specific characteristics. This study reveals that scale-dependent, monthly extreme climatic variables (e.g., maximum wind speed) and long-term, qualitative ecosystem traits (derived from text) emerge as critical drivers, in some cases surpassing the importance of traditional mean climatic indicators.To uncover these relationships, we developed a novel predictive framework by integrating 29 numerical environmental and temporal factors with unstructured textual data from 27 forest plots across China. We employed explainable automated machine learning approach and pre-trained language models to fuse these multi-modal data sources effectively. The resulting integrated model demonstrated significantly enhanced predictive performance (R2 = 0.791, RMSE = 22.775 g/m2/month, MAE = 15.951 g/m2/month) outperforming all models that relied solely on numerical data. Interpretability analysis confirmed that beyond dominant seasonal rhythms, key predictors for the model include monthly short-term extreme weather, acting as powerful triggers, and long-term, qualitative ecosystem characteristics (embedded features) that create the unique local context governing production patterns. In conclusion, this work provides both a highly accurate, interpretable tool for litterfall production prediction and a powerful, generalizable methodology for integrating qualitative knowledge into ecological processing modeling. Our findings offer critical data-driven support for advancing carbon cycle science and informing precise, climate-adaptive forest management.