An integrated multi-model approach reveals the nonlinear driving mechanisms of the ecosystem services supply-demand balance: Towards sustainable ecological management

Abstract

Exploring the nonlinear relationship and threshold between the ecosystem services (ESs) supply-demand balance and key driving factors and revealing their intrinsic mechanism is crucial for coordinating the ESs supply-demand balance and formulating sustainable management strategies. Accordingly, taking Honghe Prefecture (HHP) as an example. This study constructed a comprehensive methodological framework to comprehensively explain the nonlinear driving mechanism of ESs supply-demand balance. The research results show that: (1) From 2000 to 2020, the six ESs supply was more significant than the demand, showing a balanced state overall, but the spatial distribution differences were significant. (2) Comparing the simulation prediction results of the linear model and the machine learning (ML) model, the nonlinear machine learning models are better than the linear models, and the XGBoost has the best fitting effect. (3) The nonlinear coupling process between key driving factors and the ESs supply-demand balance was visualized through partial dependence plots (PDPs), and the optimal threshold range of key driving factors was obtained. (4) Socio-economic, landscape composition, and topographic drivers negatively impact the ESs supply-demand balance, while climate drivers positively affect ESs supply-demand. This framework has strong applicability and scalability, and can provide references for ESs Management in other regions worldwide.