Objectives  Due to the sharp decline of global ecosystem services (ESs), there is an urgent need for correct environmental governance policies. The two problems in the current ecological environment gov‍ernance in China need to be solved: (1) The lack of overall planning based on ecological function areas, (2) The shortage of in-depth synergy and tradeoff understanding among ESs. This study takes ecological function areas as the research units to measure the spatio temporal changes of four ESs (grain production (GP), carbon sequestration (CS), outdoor recreation (OR), and biodiversity conservation (BC)). It also explores the formation mechanism of the synergy and tradeoff among ESs and its spatially non-stationary response of the synergy and tradeoff to the five influencing factors of temperature, precipitation, sunshine, altitude, and urbanization.

  Methods   V arious models and multisource data are integrated to estimate the yield distribution of ESs in Fujian Province. A difference comparison method is proposed to determine the spatial distribution of the synergy and tradeoff among ESs. The Moran's I applied is to determine the spatial autocorrelation of the synergy and tradeoff among ESs. The influencing factors are select‍ed by principal component analysis, and the geographical weighted logistic regression (GWLR) determined the spatial response of the synergy and tradeoff to the influencing factors.

  Results  The main results are as follows: (1) The four ESs selected in the study (GP, CS, OR, And BC) have spatial heterogeneity. Among them, the yield distribution of GP is significantly related to the administrative region, and the highest values of BC, OR and CS appear in the ecological functional areas of biodiversity and water conservation. (2) All the Moran's I values applied are greater than 0, which means the synergy and tradeoff among ESs have significant spatial autocorrelation and are spatially clustered. (3) In space, there are synergy and tradeoff, instead of a single relationship between two ESs. (4) When the yield spatial distribution of two services is similar, such as BC and OR, the spatial distribution of synergies and tradeoffs of other services is also very similar. (5) The synergy and tradeoff among ESs showed significant responses to climate, topography, and urbanization. (6) The responses of the synergy and tradeoff among ESs to the influencing factors are spatially heterogeneous, and their positivity, negativity, and intensity varied with space.

  Conclusions  The diagnosis results of the model show that GWLR is superior to the global regression model in explaining the correlation of synergy and tradeoff among ESs with the influencing factors. By summarizing the law of the occurrence of synergy and tradeoff among ESs, this paper refines the existing formation mechanism of the synergy and tradeoff among ESs: In a certain area, the area proportion of supporting type of land and non-supporting type of land, and the degree of their support effect for each service will divide the two services into superior service and inferior service within the region. Due to the changes in land use, there are a dynamic gap between the superior service and the inferior service, and the increase in the gap may lead to tradeoff.