Interactions among ecosystem service key factors in vulnerable areas and their response to landscape patterns under the National Grain to Green Program

Abstract

A deeper understanding of the interactions between ecosystem services (ESs) and the drivers of the landscape patterns behind them is critical to how ecological restoration projects in ecologically sensitive and fragile areas are planned and managed. However, few studies have explored the response patterns of ESs to changes in landscape patterns from a geospatial perspective. This study aimed to reveal the relationship between key ESs and landscape pattern responses at spatial scales and to propose the optimization of ecological restoration measures in response to ES trade‐offs problem. The InVEST and CASA models were used to assess the spatial and temporal characteristics of five key ESs in the Beichuan River Basin since the completion of the National Grain to Green Program (2000–2020), selected five key factors are as below: habitat quality (HQ); net primary productivity (NPP); water supply (WS); soil conservation (SC); and water conservation (WC). Geographically weighted regression (GWR) models were used to reveal the trade‐offs and synergies between ESs and the driving mechanisms of landscape patterns from a geospatial perspective. The results showed that landscape connectivity in the study area improved and landscape fragmentation decreased during 2000–2020, accompanied by the increase in HQ, SC, and WC, while NPP and WS decreased. The trade‐offs between ESs appeared mainly between NPP and other ESs, with the NPP–WS pair trade‐off being the strongest. Spearman correlation analysis, geographic detectors, and GWR revealed that the dominant landscape pattern indices affecting ESs showed good agreement. The interaction of the two landscape pattern indices had stronger explanatory power for the variation in ESs than any single landscape pattern index. The patch density index, as the most important landscape pattern index affecting NPP and WS, should be the primary consideration in future landscape planning to reconcile the contradiction between WS and NPP. Because of the inherent spatial heterogeneity of ESs and landscape patterns, analyzing their response relationships from a geospatial perspective can provide scientific guidance to decision‐makers.

Where Are the Trade-Offs in Multiple Ecosystem Services in the Process of Ecological Restoration? A Case Study on the Vegetation Restoration Area in the Loess Plateau, Northern Shaanxi

“…In addition, Wang et al [68] adopted the InVEST model to probe the water yield, NPP, and soil conservation, and their results show that the NPP displays a trade-off relationship with respect to water yield, agreeing with our discoveries from the loess ridge, loess hilly, and covered sand, loess hilly areas (Figure 6). Moreover, other regions, such as arid watersheds in northwestern China [79], the Qinghai-Tibet Plateau [80], North America [81], Europe [82], and Africa [83], were also investigated using the InVEST model. Challenges still exist in this work and can be solved in our future work.…”

Analyzing Spatial–Temporal Characteristics and Influencing Mechanisms of Landscape Changes in the Context of Comprehensive Urban Expansion Using Remote Sensing

“…Research on landscape patterns predominantly concentrates on the temporal and spatial dynamics within specific regions [38][39][40] and the determinants driving these changes amidst urban expansion [29,[41][42][43][44]. However, regarding the temporal scope, prior studies have typically focused on brief periods [39] or selected years within longer intervals [29,43,44].…”

“…Research on landscape patterns predominantly concentrates on the temporal and spatial dynamics within specific regions [38][39][40] and the determinants driving these changes amidst urban expansion [29,[41][42][43][44]. However, regarding the temporal scope, prior studies have typically focused on brief periods [39] or selected years within longer intervals [29,43,44]. Yet, the evolution of landscape patterns in a region is a protracted process, influenced by sustained human activity, significant infrastructural development, and natural disasters [45].…”

“…Although most existing research focuses on the quantitative relationships between driving factors and landscape patterns [56][57][58][59], analyzing the influence mechanisms of various drivers, they often overlook the spatial impact these factors have on the driving mechanisms of landscape patterns. Given the complex spatial heterogeneity inherent in natural and social systems, drivers exert differing effects on landscape patterns depending on their geographical locations [43,60]. Wu et al found that the distribution of each landscape index had obvious spatial differentiation [61].…”