Trade-off and synergy relationships of ecosystem services and the driving forces: a case study of the Taihu Basin, Jiangsu Province

Yang, 刘洋 Liu; Jun, 毕军 Bi; Jianshu, 吕建树 Lü

doi:10.5846/stxb201808091690

Cited by 6 publications

(6 citation statements)

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“…Overall, there were synergistic relationships between ESs. Some studies on the Loess Plateau regions, Yanan city, and Gansu province also prove this conclusion [18,54]. What's more, the contributions of driving factors, including single factors and two-way interaction factors, were comprehensively evaluated for the spatio-temporal differentiation of the ESTSs.…”

Section: Discussionmentioning

confidence: 88%

“…It covered the different administrative units, such as provinces, cities, and counties, as well as urban agglomerations, economic zones (belts), agro-pastoral interlaced belts, nature reserves, ecologically fragile areas [11,12], and various geomorphological unit areas (e.g., plateau, mountain, plain, basin, etc.) [13][14][15][16][17][18]. Among them, the Yangtze River [19], the Guangdong region [20], the Three Gorges Reservoir Area mountains [21] are some of the most studied areas in China.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Sun,

Di,

Sun

et al. 2024

Land

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Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Sun,

Di,

Sun

et al. 2024

Land

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“…Based on previous studies (Jopke et al, 2015;Liu et al, 2019a;Dou et al, 2020;Huang et al, 2022) combined with the target study area and data availability, 11 drivers were selected as independent variable drivers: natural drivers, including elevation, slope, temperature, potential evapotranspiration, and precipitation; and socio-economic drivers, including gross domestic product (GDP), population density, grain production, agricultural machinery power, rural per capita net income, and general financial revenue.…”

Section: Geographical Detector and Driversmentioning

confidence: 99%

Deciphering the spatiotemporal trade-offs and synergies between ecosystem services and their socio-ecological drivers in the plain river network area

Xu,

Cheng,

Guo

et al. 2023

Front. Ecol. Evol.

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Understanding changes in ecosystem services (ESs) and quantitatively identifying the drivers that influence these changes are essential for achieving sustainable ecosystem development. In this study, multiple data sources and techniques, including meteorological data, land use/cover data, soil data, the InVEST model, and ArcGIS, were used to analyze the spatiotemporal variation characteristics of carbon storage, habitat quality, soil retention, water yield, and crop product supply in Xinghua City from 2000 to 2015. Additionally, we explored the causes of these changes and the interrelationships among these ESs. The results showed that: (1) During the study period, carbon storage and habitat quality declined, water yield fluctuated and increased, and soil retention had small interannual variations. The supply capacity of crop products first increased rapidly and then stabilized. (2) ESs were influenced by multiple drivers, with altitude having the strongest explanatory power for habitat quality and soil retention, and food production having the strongest explanatory power for crop product supply. (3) Relationships between different ESs were variable and changed over time. This study could enrich the understanding of spatial and temporal changes and drivers of ESs in the plain river network area, which has important implications for future land use planning and sustainable development of ESs.

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“…As a result of these characteristics, the Yihe River Basin serves as a pivotal study area, exemplifying the impact of landscape pattern changes on ESVs. Although the existing research methods on the response mechanism of ESV caused by changes in watershed landscape patterns are relatively mature, they are mostly concentrated in the Yangtze River, Yellow River Basin, and their tributaries [22,[45][46][47][48][49][50]. On the contrary, little attention has been paid to the empirical analysis of the relationship between landscape patterns and ESVs in the typical mountain-hill-plain areas with abundant natural resources in the Central Plains [51], which need further study.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

Han,

Li,

Wei

et al. 2023

Forests

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Identifying the changes in landscape pattern and ecosystem service value (ESV) and clarifying their relationship in temporal changes and spatial variations can provide insight into regional landscape features and scientific support for regional landscape planning. Leveraging land use data from the Yihe River Basin, we quantitatively assessed the landscape pattern and ESV shifts spanning from 2000 to 2018 using the landscape pattern indexes and the equivalence factor method. We employed Pearson correlation metrics and the geographically weighted regression model to explore the interrelation of their spatiotemporal variations. Our results show the following: (1) Forestland represents the most expansive land cover category. Apart from construction land, all other types experienced a decline in area. The most notable change occurred in the area of construction land. (2) The aggregation of the overall landscape shows a downward trend. The levels of fragmentation, landscape diversity, and richness increased. (3) Throughout the entire study period, the overall ESV gradually decreased, and the land cover type with the greatest contribution to the ESV was forestland. (4) In terms of temporal changes, the patch density and edge density of the overall area are significantly negatively correlated with total ESVs. The largest values for the patch index, perimeter–area fractal dimension (PAFRAC), and aggregation are significantly positively correlated with total ESVs. (5) In terms of spatial variation, the contagion index (CONTAG), PAFRAC, and the Shannon diversity index (SHDI) were noticeably correlated with ESVs. The CONTAG is positively correlated with ESVs upstream, but negatively midstream and downstream. The SHDI is negatively correlated with ESVs upstream, but positively midstream and downstream. The PAFRAC exhibits a positive correlation with ESVs for the most part. The association between the landscape pattern indexes and ESVs exhibits temporal and spatial inconsistencies in most instances, suggesting a spatiotemporal scale effect in their relationship. This study recommends that the local government devises a long-term strategy for urban development and exercises stringent control over the unregulated expansion of construction land. Through reasonable territorial spatial planning, government departments could enhance the connectivity of the overall landscape pattern of the Yihe River Basin to achieve the reasonable allocation and sustainable development of regional resources.

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Trade-off and synergy relationships of ecosystem services and the driving forces: a case study of the Taihu Basin, Jiangsu Province

Cited by 6 publications

References 0 publications

Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

Deciphering the spatiotemporal trade-offs and synergies between ecosystem services and their socio-ecological drivers in the plain river network area

Identifying the Relationships between Landscape Pattern and Ecosystem Service Value from a Spatiotemporal Variation Perspective in a Mountain–Hill–Plain Region

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