Trade-offs and synergies in ecosystem services for the Yinchuan Basin in China

Wang, Wei

doi:10.1016/j.ecolind.2017.10.001

Cited by 80 publications

(37 citation statements)

References 41 publications

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“…Quantification and mapping of ESs are the foundation of integrating ecosystem services into management planning and decision making (de Groot et al, 2010;Grêt-Regamey, Altwegg, Sirén, van Strien, & Weibel, 2017). The calculation method of ESs is applicable to other areas, thus this analysis result is comparable with those presented in previous studies, i.e., carbon sequestration evaluated for the Guanzhong-Tianshui economic region (Zhou et al, 2017), sand fixation in Yinchuan Plain (Li & Wang, 2018), and food production in the Three Parallel Rivers Region (Lin et al, 2018).…”

Section: Es Correlations With Driving Mechanismssupporting

confidence: 70%

“…The northern and southern mountains had the largest supply of sand fixation due to the steep terrain and higher humidity, which is critical in northwestern China (Li & Wang, 2018). It also maintains large areas of native vegetation and intact natural environment with high forest biomass, high biodiversity, valuable scenery (Lin et al, 2016), and high carbon density (Buczko et al, 2017) and has moderate potential for recreational opportunity and carbon storage, which is consistent with the results in an Alpine region (Zoderer, Tasser, Erb, Lupo Stanghellini, & Tappeiner, 2016), and in the Three Parallel Rivers Region (Lin et al, 2018).…”

Section: Es Bundles Corresponding To Socio-ecological Clustersmentioning

confidence: 99%

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Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

et al. 2019

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A B S T R A C TUnderstanding the spatial distributions of multiple ecosystem services (ESs), their associations, and their underlying socio-ecological contributing factors is critical for ES management. Using the city belt along the Yellow River in Ningxia, northwestern China, as a case study, this study quantified the spatial distribution of six ESs (food production, carbon sequestration, carbon storage, nutrient retention, sand fixation and recreational opportunity), analyzed the synergy and trade-off relations among them through correlation analysis, classified ES bundles through a self-organizing map method (SOM), explored the impacts of socio-ecological factors on the ESs through Ordinary Least Square regression (OLS) and Geo-detector analysis, delineated socio-ecological clusters using the SOM, and characterized the relationship between ES bundles and driver clusters through overlap analysis. The results suggest that spatial associations among ESs can be predicted by their driving mechanisms. Synergy relations existed among crop production, carbon sequestration, carbon storage and nutrient retention, and these were impacted by similar driving mechanisms. Synergy also existed between sand fixation and recreational opportunity, but significant differences existed in their driving mechanisms. Trade-off relations were shown between ESs in these two groups at the whole region scale. Three bundles were detected among the six ESs: bundle 1, characterized by recreational opportunity of high supply and other services of limited supply, was located in the transitional region between the central plain and the fringe mountains, and mainly driven by climate and proximity factors; bundle 2, characterized by high sand fixation, medium carbon storage and limited other services, was located in the northwestern and southern mountains and driven by climate and geography factors; bundle 3, characterized by high food production, carbon sequestration, carbon storage and nutrient retention of medium supply and other two services of limited supply, was located in the central plain and driven by vegetation coverage and proximity factors. Human activities can partly overcome the limitations of ecological conditions, thus specific strategies for different regions are proposed to maintain and improve ESs under global climate change.

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Section: Es Correlations With Driving Mechanismssupporting

confidence: 70%

Section: Es Bundles Corresponding To Socio-ecological Clustersmentioning

confidence: 99%

Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

et al. 2019

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“…Ecosystem services are the benefits that people can obtain from ecosystems [19]. An evaluation model of the changes in ESV based on LUCC has been widely used [20][21][22][23]. In addition, ecological footprint (EF) and biocapacity (BC) are also important measurements of the ecological suitability of national and regional economic development [24,25].…”

Section: Introductionmentioning

confidence: 99%

Ecological Suitability of Island Development Based on Ecosystem Services Value, Biocapacity and Ecological Footprint: A Case Study of Pingtan Island, Fujian, China

et al. 2020

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The ecological environment and resource endowment of an island are more vulnerable compared to the mainland, and special assessment and measurement of the ecological suitability for development are significant. Pingtan Island (Fujian, China) was taken as a case study. Changes in ecosystem services value and the profit-and-loss balance between ecological footprint and biocapacity were assessed using land use/cover changes based on remote-sensing images taken in 2009, 2014 and 2017, and the ecological suitability of development was measured. Results show that island development led to a decrease in the ecosystem services value and an increase in ecological footprint and biocapacity. The key ecological factors restricting the scale of island development are topography, vegetation with special functions and freshwater. Biocapacity of islands can increase not only by changing from lower-yield land types to higher-yield construction land types but also by external investment. A new measurement framework was proposed that simply and clearly reveals the ecological suitability of island development and the underlying key constraints.

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“…Applying models can be the alternative to overcome the spatio-temporal limits of the fluxes. Both satellite-based and process-based ecosystem models were applied to simulate productivities in the Northeast Asian MLE [15,16,[28][29][30]. However, these modeling approaches had limitations in reflecting management details because of different parametrizations and representations of ecological processes and spatio-temporal data limitations.…”

Section: Introductionmentioning

confidence: 99%

Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)

Song

Pietsch

Kim

et al. 2019

Forests

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The mid-latitude ecotone (MLE)—a transition zone between boreal and temperate forests, which includes the regions of Northeast Asia around 30°–60° N latitudes—delivers different ecosystem functions depending on different management activities. In this study, we assessed forest volume and net primary productivity changes in the MLE of Northeast Asia under different ecological characteristics, as well as various current management activities, using the BioGeoChemistry Management Model (BGC-MAN). We selected five pilot sites for pine (Scots pine and Korean red pine; Pinus sylvestris and P. densiflora), oak (Quercus spp.), and larch forests (Dahurian larch and Siberian larch; Larix gmelinii and L. sibirica), respectively, which covered the transition zone across the MLE from Lake Baikal, Russia to Kyushu, Japan, including Mongolia, Northeast China, and the Korean Peninsula. With site-specific information, soil characteristics, and management descriptions by forest species, we established their management characteristics as natural preserved forests, degraded forests, sandy and cold forest stands, and forests exposed to fires. We simulated forest volume (m3) and net primary productivity (Mg C ha−1) during 1960–2005 and compared the results with published literature. They were in the range of those specified in previous studies, with some site-levels under or over estimation, but unbiased estimates in their mean values for pine, oak, and larch forests. Annual rates of change in volume and net primary productivity differed by latitude, site conditions, and climatic characteristics. For larch forests, we identified a high mountain ecotype which warrants a separate model parameterization. We detected changes in forest ecosystems, explaining ecological transition in the Northeast Asian MLE. Under the transition, we need to resolve expected problems through appropriate forest management and social efforts.

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Trade-offs and synergies in ecosystem services for the Yinchuan Basin in China

Cited by 80 publications

References 41 publications

Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia, China

Ecological Suitability of Island Development Based on Ecosystem Services Value, Biocapacity and Ecological Footprint: A Case Study of Pingtan Island, Fujian, China

Assessing Forest Ecosystems across the Vertical Edge of the Mid-Latitude Ecotone Using the BioGeoChemistry Management Model (BGC-MAN)

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