Evidence map›Paper›PMID 40328950›Full record

ArticleScientific reports2025

Spatiotemporal effects of landscape structure on the trade-offs and synergies among ecosystem service functions in Yangtze River Economic Belt, China.

Yao Yao, Yuanyuan Yang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yao YaoSchool of Public Management, Guizhou University of Finance and Economics, Guiyang, 500025, China.
Yuanyuan YangSchool of Tourism Management, Guizhou University of Commerce, Guiyang, 550014, China. 739271126@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective landscape management is vital for maximizing the availability of ecosystem service functions (ESF). However, how landscape pattern simultaneously affects multiple ESF across time and space, specifically regarding changes in their relationships, has not been comprehensively evaluated. Accordingly, the current study examines the dynamic impact of landscape pattern on the interactions among ES trade-offs in China's Yangtze River Economic Belt (YREB) from 1990 to 2020. Five key ESF are evaluated: carbon sequestration (CS), food supply (FS), habitat quality (HQ), water retention (WR), and soil conservation (SC). The geographical and temporal weighted regression model is adopted to analyze how these relationships are influenced by changes in the landscape pattern over 30 years. Over the past three decades, the overall amounts of FS, WR, and SC have increased, with only WR exhibiting a sustained increasing trend, while CS and HQ have significantly decreased. The trade-off coordination effects among the ESF generally transitioned from trade-offs to synergies, with the CS-FS change more pronounced, shifting from weak trade-offs (- 0.29) to synergies (0.20). Significant spatial variations were also observed. Most landscape pattern significantly impact the relationships among ESF. Specifically, landscape composition has a stronger influence on ecosystems than configuration; however, these effects are diverse and often inconsistent, requiring decision-makers to continuously balance landscape management. The impacts of landscape pattern on the relationships between SC and other ESF (CS-SC, HQ-SC, HQ-SC, and WR-SC) were consistent, with the most prominent impact on CS-SC. This suggests that landscape pattern exerts a comparatively steady impact on CS, benefiting from the contributions of decision-makers to soil conservation in regional land parcels. These results underscore the intricate responses of inter-ecosystem relationships to landscape pattern and the need for dynamic trade-offs in landscape management. Cumulatively, this study provides crucial guidance for policymakers in designing effective landscape management policies to mitigate ecological degradation.

Indexed as

Conservation of Natural ResourcesEcosystemRiversCarbon SequestrationChinaFood SupplySoilSpatio-Temporal AnalysisSoilEcosystem service functionsLandscape patternSustainable landscape managementSynergyTrade-offYangtze River Economic Belt

Identifiers

PMID40328950
PMCPMC12056077

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.