Evidence map›Paper›PMID 42243396›Full record

ArticleScientific reports2026

Spatiotemporal dynamics and nonlinear drivers of ecosystem service synergies and trade-offs in Shaanxi, China.

Menghan Gao, Khai Ern Lee, Aida Soraya Shamsuddin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Menghan GaoInstitute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Khai Ern LeeInstitute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia. khaiernlee@ukm.edu.my.
Aida Soraya ShamsuddinInstitute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

Funding

Ministry of Higher Education, Malaysia Konsortium Kecemerlangan Penyelidikan (KKP/2021/UKM- UKM/1/1)
6 · The paper itself

Abstract

Spatiotemporal interactions among ecosystem services (ES) and their drivers are essential for sustainable landscape management. This study employs the Dynamic Ecosystem Trade-off Synergy Index (DETSI), local indicators of spatial association (LISA) and interpretable machine learning (XGBoost-SHAP) to assess ES synergies and trade-offs, examine nonlinear driver-response patterns, and identify threshold effects in Shaanxi Province, China, from 2000 to 2022. Six ES indicators, net primary productivity (NPP), habitat quality (HQ), water yield (WY), soil retention (SR), cultural landscape (CL) and food supply (FS), were analysed to characterise spatial patterns and temporal dynamics. Results show persistent south-north gradients and stable high-value clusters in ecological core zones. Regulating service pairs exhibit strong synergies, whereas cultural-provisioning and provisioning-regulating pairs show persistent trade-offs. From 15 ES pairs, five representative pairs (CL-WY, FS-WY, NPP-SR, NPP-WY and SR-WY) were examined using SHAP, achieving accuracy of 0.8112-0.9696 and macro-F1 of 0.8110-0.9603. Eighteen key drivers, including NDVI, precipitation, elevation, road density and points of interest, show nonlinear and threshold-like responses. Moderate conditions support synergies, while extremes drive decoupling. The Qinling-Daba corridor emerges as a stable synergy zone, with northern and southeastern Shaanxi identified as recovery and rupture areas. This framework provides a robust transferable approach for spatially differentiated ecological assessment and planning.

Indexed as

Conservation of Natural ResourcesEcosystemChinaFood SupplySoilSpatio-Temporal AnalysisSoilDynamic ecosystem trade-off synergy index (DETSI)Ecosystem servicesExtreme gradient boosting (XGBoost)Local indicators of spatial association (LISA)Shapley additive explanations (SHAP)

Identifiers

PMID42243396
PMCPMC13473471

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Registered trials

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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.