Trade-offs and driving factors of multiple ecosystem services and bundles under spatiotemporal changes in the Danjiangkou Basin, China

Zhang, Jian; Cheng, Changjin; Tang, Zhiying; Qi, Lianghua

doi:10.1016/j.ecolind.2022.109550

Cited by 10 publications

(6 citation statements)

References 62 publications

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“…The main reason for this is that the land use type in this area is dominated by construction land and agricultural land, which is more affected by human interference, with lower vegetation cover, less interception of precipitation, and flatter topography, so the integrated ESs are weaker. This is consistent with the experience of previous studies [57,66,67]. The areas with high values of composite ESs are mainly located near the ecological barriers of Zhangguangcai Range and Xiaoxing'an Mountains, where forest, as the main land use type, has a high CS.…”

Section: Spatial Pattern and Evolution Characteristics In Lui And Esssupporting

confidence: 92%

Impacts of Land Use Intensity on Ecosystem Services: A Case Study in Harbin City, China

Qi,

Wang,

Shen

et al. 2023

Sustainability

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Land use intensity (LUI) is an important indicator for assessing human activities, and quantitatively studying the impact of LUI on ESs can help to realize the scientific management of urban ecosystems as well as sustainable development. In this study, we quantified five important ecosystem service bundles in the study area with the aid of the R-language “kohonen” package and used bivariate spatial autocorrelation modeling to examine the effects of LUI on the ESs in Harbin City from 2000 to 2020. These ESs include food supply (FP), water conservation (WC), soil conservation (SC), carbon storage (CS), water purification (WP), and habitat quality (HQ). The results show the following: (1) The LUI in Harbin City had a trend from 2000 to 2020 of “decreasing and then growing”, with a spatial distribution pattern of “high in the west and low in the east.” (2) Except for FP, all other ESs exhibit a similar spatial pattern of “west-low-east-high”; WC and WP exhibit a trend of continuous increase, SC exhibits a trend of decreasing and then increasing, and CS and HQ are generally more stable, with less fluctuation. The built-up area is situated in the high-value area of LUI, and the area exhibits a significant expansion trend. (3) Ecological conservation bundles, FP–WP synergistic bundles, ecological transition bundles, CS–WP–HQ synergistic bundles, and FP bundles are the five ecosystem service bundles that were discovered in Harbin. (4) From 2000 to 2020, there is a predominately “low LUI-high ESs” and “high LUI-low ESs” aggregation type, with a substantial positive correlation between LUI and FP and a significant negative correlation between LUI and other ESs. Harbin City should strengthen the management of ESs in the western part of the city and, at the same time, maintain the favorable ecological conditions in the ecological barriers of Zhangguangcai Range and Xiaoxing’an Mountains.

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Section: Spatial Pattern and Evolution Characteristics In Lui And Esssupporting

confidence: 92%

Impacts of Land Use Intensity on Ecosystem Services: A Case Study in Harbin City, China

Qi,

Wang,

Shen

et al. 2023

Sustainability

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“…Recent studies have also indicated that ESs respond positively or negatively to socioecological factors [35][36][37]. However, the response processes of ESs (including supply, demand, and supply-demand balance) to socioecological factors should be accounted for because complex nonlinear relationships between ESs and driving factors have been detected [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

Zhang,

Fang,

Yao

et al. 2024

Land

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Understanding the response of the mechanisms driving ecosystem services (ESs) to socioecological factors is imperative for regional sustainable ecosystem management. However, previous studies of the mechanisms driving ESs have focused more on the degree and direction (positive and negative) of effects on ES supply or the supply–demand balance, while their nonlinear response processes have not been fully considered. In this study, a theoretical framework was developed through integrating land use/land cover data and supply and demand matrices with random forest models to assess response processes, including the relative importance and marginal effects, of essential factors that drive ES demand, supply, and supply–demand balance. Using the Yangtze River Economic Belt (YREB) as an example, our results indicated that the ES deficit regions (332 of 1070 counties or 14.45% of the area) of the YREB were located mainly in the three national urban agglomerations. Moreover, this study indicated that natural environmental factors (such as slope and precipitation) significantly influence the supply and supply–demand balance of ESs, while socioeconomic factors (such as cropland ratios and population density) profoundly influence the demand for ESs. However, cropland ratios were the most important drivers of ES supply, demand, and supply–demand balance in the YREB. Moreover, three types of response processes were identified in this study: logarithmic increase, logarithmic decrease, and volatility increase. Specific driving factors (e.g., proportion of cropland area, precipitation, population density, and slope) had significant threshold effects on the supply–demand balance of ESs. The turning points that can be extracted from these response processes should be recommended for ecosystem restoration projects to maintain regional sustainable ecosystem management.

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“…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%

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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Trade-offs and driving factors of multiple ecosystem services and bundles under spatiotemporal changes in the Danjiangkou Basin, China

Cited by 10 publications

References 62 publications

Impacts of Land Use Intensity on Ecosystem Services: A Case Study in Harbin City, China

Impacts of Land Use Intensity on Ecosystem Services: A Case Study in Harbin City, China

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

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

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