Guiding and constraining reclamation for coastal zone through identification of response thresholds for ecosystem services supply–demand relationships

Abstract

Enhancing the understanding and management of ecosystem services (ESs) supply and demand under the influence of global change is crucial for sustaining human well‐being and promoting sustainable development. However, the comprehension of changes in ecosystem service dynamics in the face of complex human disturbances remains limited. This study focuses on four representative ESs in the northern Liaodong Bay, a typical global estuary wetland, to elucidate alterations in the supply and demand of these ESs and comprehend the impact mechanisms arising from reclamation activities. The findings reveal the following key insights: between 1993 and 2020, the supply and demand for water yield disparity became increasingly pronounced, while the equilibrium between supply and demand for carbon storage shifted from surplus to balance. In contrast, the excess supply–demand of food supply and landscape esthetics remained relatively consistent. This study delves deeper into the non‐linear relationship between reclamation activities and ESs supply–demand relationships, with each ecosystem service exhibiting distinct patterns. As reclamation intensity rises, the supply–demand ratio of ESs exhibits varying trends: food supply and carbon storage demonstrate an inverted U‐shaped relationship, water yield follows a U‐shaped pattern, and landscape esthetics progressively decline. By identifying reclamation thresholds, different management strategies should be applied to areas exceeding and falling below these thresholds, with constraints and guidance being necessary for the former and the latter, respectively. These findings advance our understanding of ESs dynamics in the context of human‐driven changes and provide practical implications for sustainable ecosystem management in coastal areas.

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

“…Many studies have shown that the random forest model is a machine learning algorithm based on statistics that can quantify the relative importance of driving factors and their nonlinear relationships, revealing their threshold effects and effectively compensating for the shortcomings of traditional methods [16,28]. The random forest model performs well in identifying the driving mechanisms of specific ESs, but research on the driving mechanisms of the supply and demand bal-ance of multiple ESs and the threshold effects needs to be strengthened [43]. Few studies have focused on the nonlinear response processes of multiple ES supply, demand, and supply-demand balance mechanisms to dominant factors when considering the mechanisms driving ESs [9,28,44].…”