Assessing the destabilization risk of ecosystems dominated by carbon sequestration based on interpretable machine learning method

Increasing carbon sequestration (CS) in soils and biomass is an important land-based solution in mitigating global warming. Ecosystems provide a wide range of ecosystem services (ESs). The necessity to augment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to evaluate the destabilization risk resulting from alterations in ecosystem service relationships dominated by CS. We selected Northeastern China as study area to estimate six ESs and identified areas of destabilization risk among the three services most relevant to CS, including food production (FP), soil retention (SR), and habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and introduced the Shapley additive interpretation (SHAP) method for driving mechanism analysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of CS, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destabilization risk. The study has potential practical value for destabilization risks prevention, while also providing a scientific basis for formulating comprehensive carbon management policies and maintaining ecosystem stability.