Revealing Impacts of Human Activities and Natural Factors on Dynamic Changes of Relationships among Ecosystem Services: A Case Study in the Huang-Huai-Hai Plain, China

Abstract

Understanding the dynamic changes of relationships between ecosystem services (ESs) and their dominant factors can effectively adjust human activities to adapt proactively to global climate change. In this study, the Huang-Huai-Hai Plain (HHHP) was selected to assess the dynamics of four key ESs (NPP, net primary productivity; WY, water yield; SC, soil conservation; FP, food production) from 2000 to 2020. The constraint lines of interactions among ESs were extracted based on a segmented quantile regression model. On this basis, the effects of both human activities and natural factors on the key features of the interactions between ESs were quantified with the help of automatic linear model. The results indicated that two types of constraint relationships, including exponential and humped-shaped, existed among the six pairs of ESs. In the past two decades, small changes in NPP thresholds would lead to large variations in other ESs thresholds. Precipitation and normalized difference vegetation index were the key factors to determine the constraint strength of ESs in the HHHP. The potential maximum value of WY in the HHHP could be increased by adjusting landscape shape to make it more complicated. This study helps to improve the potential of target ESs and provides a decision-making basis for promoting regional sustainable development.

Spatiotemporal heterogeneity in ecosystem service trade-offs and their drivers in the Huang-Huai-Hai Plain, China

“…Between 2000 and 2020, there was a noticeable increase in land occupied by built-up areas, rising from 13.14 to 18.13%, consequently exerting mounting pressure on the ecological environment. The conflict between promoting regional socio-economic development and ensuring ecological environmental conservation has emerged as a significant constraint on the HHHP's sustainable development, leading to adverse consequences for the local community welfare (Deng et al 2022b).…”

“…Considering the natural characteristics and socioeconomic conditions of the HHHP, and drawing from prior studies (Deng et al 2022b), this study identified several potential driving factors (refer to Table 2) and utilized the ALM model (Zhang et al 2021a) to analyze the primary factors influencing the ES trade-offs. Through the removal of irrelevant variables and the management of multicollinearity among factors, the ALM model produced optimal regression outcomes.…”

“…ESs comprise provisioning, supporting, regulating, and cultural services (MA 2005). In recent years, approximately 60% of ESs globally have suffered severe degradation due to intense anthropogenic disturbances and specific demands on ES types (Deng et al 2022b), posing a serious threat to ecosystem security. In this context, adopting reasonable management measures becomes crucial to regulate and optimize ESs overall, thereby improving ecosystem sustainability.…”

“…From a research standpoint, the focus of ES trade-offs primarily centers on a static perspective, analyzing spatial patterns and driving factors at specific times to offer recommendations for ecosystem management (Hd et al 2020). It is important to note that ES tradeoffs may vary over time (Deng et al 2022b). To foster ecosystem sustainability, we must analyze the tradeoffs across multiple periods of ESs while also identifying the shifts in spatial and temporal variations among these relationships.…”

Evolution and attribution of the water yield coefficient in the Yiluo river basin

“…For identification of attributes related to water yield capacity, common methods include scenario analysis (Yang J. et al, 2021;Shao and Yang, 2021), model analysis (Sun et al, 2019;Wang X. et al, 2022), and statistical analysis (Rizzo et al, 2020;Deng et al, 2022). Dai and Wang (2020) studied the influence of climate factors and land use factors on water yield using the geographic detector model and reported that precipitation and evapotranspiration were the primary driving factors in the Hengduan Mountain region.…”