Spatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: Implications for spatial planning and management

Xia, Hao; Yuan, Shengtao; Prishchepov, Alexander V.

doi:10.1016/j.resconrec.2022.106767

Cited by 166 publications

(45 citation statements)

References 66 publications

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“…We used geographically weighted regression (GWR) to determine the spatial interaction of ES pairs and the spatial driving mechanism of the landscape pattern index on the ES. Using the GWR model, the correlation degree of dependent variables and independent variables under different spatial geographic locations can be clarified, which helps to further understand their spatial variation relationships (Ma et al, 2022;Xia et al, 2023;Xue et al, 2023). The results of GWR are consistent with the correlation analysis, that is, there is a positive effect between the two variables when the regression coefficient is >0, and a negative effect vice versa:…”

Section: Geographical Weighted Regressionsupporting

confidence: 58%

“…WC indicates the water storage capacity of different ecosystems per unit of time and is an important indicator of regulating services (Xia et al, 2023). It can be calculated based on the results of water production by the following equation (Li, Liang, et al, 2021):…”

Section: Water Conservationmentioning

confidence: 99%

“…Factor detection is performed by expressing the extent to which different landscape pattern indices explain the spatial heterogeneity of an ES through the q-value magnitude (Ma et al, 2022;Xia et al, 2023):…”

Section: Geographic Detectormentioning

confidence: 99%

“…Ecosystem services (ESs) are various functions and products provided by ecosystems that can contribute to the well-being of human society, such as air purification, water regulation, soil conservation (SC), biodiversity maintenance, food production, and cultural services (Bai et al, 2020;Ma et al, 2022;Millennium Ecosystem Assessment, 2005;Xia et al, 2023;Yu et al, 2023). Approximately 60% of global ESs are in decline due to climate change and increased anthropogenic disturbances (Vihervaara et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Zuo,

Gao,

2023

Land Degrad Dev

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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.

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Section: Geographical Weighted Regressionsupporting

confidence: 58%

Section: Water Conservationmentioning

confidence: 99%

Section: Geographic Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zuo,

Gao,

2023

Land Degrad Dev

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“…The trade-off between ecosystem services is spatially heterogeneous and changes with the passage of spatial scales [73]. Therefore, there is a need to link scale effects with specific geographical environments to provide insights for cross-scale ecosystem management and sustainable spatial planning [72,74,75]. According to our methods, weak synergies meant that at least one service was low (L) and lacking ESs at the high (H) level, which underlined the necessity of land management.…”

Section: Multiscale Characteristics Of Ess Trade-offs and Synergiesmentioning

confidence: 96%

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Ding

Yan

Zhu

et al. 2023

Land

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Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.

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Dynamic interactions and mechanisms between high‐quality urbanization and ecosystem service value in Shandong Province: Trends, coordination, and implications for sustainable development

Liu,

Ren

2024

Land Degrad Dev

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An extensive and profound investigation into the dynamic interactions between High‐Quality Urbanization (HQU) and Ecosystem Service Value are of utmost importance for the coordinated development of the region. Diverging from traditional macro‐level analysis, this study is grounded on county‐scale data, measuring HQU and ecosystem service value from a multidimensional perspective. On the basis of the research framework of “phenomena‐relationships‐mechanisms”, we expect to delve deeper into the spatio‐temporal evolutionary characteristics of ecosystem service value and HQU and their integrated interactions. Additionally, we utilize econometric models to rigorously evaluate the impact of HQU on ecosystem service value, with the goal of filling the current research gaps. The research results are as follows: (1) From 2000 to 2020, the ecosystem service value in Shandong Province exhibit an overall decreasing and then increasing trend, and shows a spatial posture of high east and low west. HQU generally maintains a steady upward trend, and its spatio‐temporal evolution has apparent heterogeneity. (2) The Coupled Coordination Degree (CCD) between HQU and ecosystem service value has a gradual rising trend, and there is an increasing level of consistency between them. (3) HQU has a negative effect on ecosystem service value, and the direction and degree of the effect on segmented ecosystem service value types vary greatly. (4) The effect of HQU on ecosystem service value is heterogenous, and the attenuating effect is mainly found in grain‐producing counties, inland counties, and counties with a lower index of high‐quality urbanization. In conclusion, our results are expected to provide targeted recommendations for sustainable development and ecological balance in Shandong Province.

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Spatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: Implications for spatial planning and management

Cited by 166 publications

References 66 publications

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

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

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Dynamic interactions and mechanisms between high‐quality urbanization and ecosystem service value in Shandong Province: Trends, coordination, and implications for sustainable development

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