Evidence map›Paper›PMID 42045404›Full record

ArticleScientific reports2026

Spatial heterogeneity, trade-offs, and bundle identification of ecosystem services in karst watersheds: a comprehensive assessment of the Lijiang River Basin.

Yali Feng, Yanling Huang, Zhongjun Wu, Ruize Sun, Yongkang Wang

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.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

5 authors.

Yali FengCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China. mingji2046@163.com.
Yanling HuangCollege of Tourism & Landscape Architecture, Guilin University of Technology, Guilin, 541006, China. mingji1203@126.com.
Zhongjun WuCollege of Tourism & Landscape Architecture, Guilin University of Technology, Guilin, 541006, China.
Ruize SunCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.
Yongkang WangCollege of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541006, China.

Funding

Guangxi Science and Technology Program Guike AD25069074National Natural Science Foundation of China 4256010361
6 · The paper itself

Abstract

The Lijiang River Basin is under pressure from rapid urbanization and tourism. To support sustainable management, the present study assessed 10 ecosystem services (ESs) from 2000 to 2020 using the InVEST model and spatial statistics. Correlation analysis and a Self-Organizing Map neural network were combined to analyze ES interactions and identify service bundles, whereas Geodetector revealed driving factors. There was significant spatial differentiation of the services, with high-value areas for carbon sequestration, habitat quality, food production, and soil conservation in high-altitude forest regions, whereas low-altitude regions showed high water yield, recreational services, and nutrient retention due to human activity. There were notable interactions among the services, with most regulating services exhibiting spatial synergies, particularly in mountainous regions, while water purification faced trade-offs with other services. Five typical service bundles were identified, reflecting the interplay of natural resilience and human impact. Land use was the primary driver of service differentiation, with an increasing influence of socioeconomic factors. Based on the results, the study recommends differentiated governance strategies: strict protection of ecological supply zones and optimization of pressure response zones, which provide a scientific basis for ecological security and land planning in karst regions.

Indexed as

Driving factorsEcosystem service bundlesEcosystem servicesLijiang River BasinTrade-offs and synergies

Identifiers

PMID42045404
PMCPMC13287675

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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.