This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to ...This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to becoming carbon sources under compounded bioclimatic stress.BEAT synthesizes satellite-derived data from 2001 to 2022 and integrates temperature anomalies,vapor pressure deficit(VPD),fire activity,and vegetation degradation into a Compound Stress Index(CSI).Methodologically,the study applies piecewise regression,changepoint analysis,and early warning signal(EWS)metrics,including rolling variance and lag-1 autocorrelation,to identify nonlinear emission tipping points and ecological resilience loss.Machine learning models such as XGBoost and SHAP were employed to evaluate the predictive relevance of CSI components and enhance model interpretability.Results reveal a critical CSI threshold(≥0.6),beyond which Net Ecosystem Exchange(NEE)exhibits abrupt positive anomalies,indicating carbon emission amplification.EWS metrics significantly increased prior to emission spikes,validating BEAT’s predictive capacity for ecological destabilization.In addition,spatial clustering and time-lagged correlation analysis confirmed the alignment between compound stress hotspots and emission anomalies,and when compared to traditional Earth System Models(ESMs),BEAT uniquely captures synergistic stress interactions and nonlinearity.The findings underscore BEAT’s potential to improve early warning systems,REDD+monitoring frameworks,and climate adaptation planning.Its scalable design enables application across vulnerable biomes globally and offers a transformative tool for anticipating biosphere-climate tipping points and informing proactive ecosystem governance.展开更多
文摘This study introduces the Bioclimatic Emission Amplification Theory(BEAT),a novel framework for detecting and forecasting how terrestrial ecosystems,particularly the Amazon Basin,transition from being carbon sinks to becoming carbon sources under compounded bioclimatic stress.BEAT synthesizes satellite-derived data from 2001 to 2022 and integrates temperature anomalies,vapor pressure deficit(VPD),fire activity,and vegetation degradation into a Compound Stress Index(CSI).Methodologically,the study applies piecewise regression,changepoint analysis,and early warning signal(EWS)metrics,including rolling variance and lag-1 autocorrelation,to identify nonlinear emission tipping points and ecological resilience loss.Machine learning models such as XGBoost and SHAP were employed to evaluate the predictive relevance of CSI components and enhance model interpretability.Results reveal a critical CSI threshold(≥0.6),beyond which Net Ecosystem Exchange(NEE)exhibits abrupt positive anomalies,indicating carbon emission amplification.EWS metrics significantly increased prior to emission spikes,validating BEAT’s predictive capacity for ecological destabilization.In addition,spatial clustering and time-lagged correlation analysis confirmed the alignment between compound stress hotspots and emission anomalies,and when compared to traditional Earth System Models(ESMs),BEAT uniquely captures synergistic stress interactions and nonlinearity.The findings underscore BEAT’s potential to improve early warning systems,REDD+monitoring frameworks,and climate adaptation planning.Its scalable design enables application across vulnerable biomes globally and offers a transformative tool for anticipating biosphere-climate tipping points and informing proactive ecosystem governance.
