Drivers of Structural and Functional Resilience Following Extreme Fires in Boreal Forests of Northeast China

Abstract

Ongoing climate change has intensified fire disturbances in boreal forests globally, posing significant risks to forest ecosystem structure and function, with the potential to trigger major regime shifts. Understanding how environmental factors regulate the resilience of key structural and functional parameters is critical for sustaining and enhancing ecosystem services under global change. This study analyzed the resilience of forest ecosystems following three representative extreme fires in the Greater Xing’an Mountains (GXM) via the temporal evolution of the leaf area index (LAI), net primary productivity (NPP), and evapotranspiration (ET) as key indicators. A comprehensive wall-to-wall assessment was conducted, integrating gradient boosting machine (GBM) modeling with Shapley Additive Explanation (SHAP) to identify the dominant factors influencing postfire resilience. The results revealed that NPP demonstrated stronger resilience than ET and LAI, suggesting the prioritization of functional restoration over structural recovery in the postfire landscape of the GXM. The GBM-SHAP model explained 45% to 69% of the variance in the resilience patterns of the three parameters. Among the regulatory factors, extreme precipitation and temperature during the growing season were found to exert more significant influences on resilience than landscape-scale factors, such as burn severity, topography, and prefire vegetation composition. The spatial asynchrony in resilience patterns between structural and functional parameters highlighted the complex interplay of climatic drivers and ecological processes during post-disturbance recovery. Our study emphasized the importance of prioritizing functional restoration in the short term to support ecosystem recovery processes and services. Despite the potential limitations imposed by the coarse spatial granularity of the input data, our findings provide valuable insights for postfire management strategies, enabling the effective allocation of resources to increase ecosystem resilience and facilitating long-term adaptation to changing fire regimes.

Keywords

• ecosystem resilience
• extreme fires
• driving factors
• machine learning
• Eurasian boreal forest