Evidence map›Paper›PMID 35505680›Full record

ArticlePeerJ2022

Quantitative analysis of vegetation restoration and potential driving factors in a typical subalpine region of the Eastern Tibet Plateau.

Yu Feng, Juan Wang, Qin Zhou, Maoyang Bai, Peihao Peng, Dan Zhao, Zengyan Guan, Xian'an Liu

Abstract read
In one paragraph

Article in PeerJ, 2022. 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

8 authors.

Yu FengCollege of Earth Sciences, Chengdu University of Technology, Chengdu, China.
Juan WangCollege of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, China.
Qin ZhouChengdu OCI Medical Devices Co., Ltd, Chengdu, China.
Maoyang BaiCollege of Earth Sciences, Chengdu University of Technology, Chengdu, China.
Peihao PengCollege of Earth Sciences, Chengdu University of Technology, Chengdu, China.
Dan ZhaoSchool of Tourism and Culture Industry, Sichuan Tourism University, Chengdu, China.
Zengyan GuanCollege of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, China.
Xian'an LiuCollege of Art, Sichuan Tourism University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vegetation restoration is an essential approach to re-establish the ecological balance in subalpine areas. Changes in vegetation cover represent, to some extent, vegetation growth trends and are the consequence of a complex of different natural factors and human activities. Microtopography influences vegetation growth by affecting the amount of heat and moisture reaching the ground, a role that is more pronounced in subalpine areas. However, little research is concerned with the characteristics and dynamics of vegetation restoration in different microtopography types. The respective importance of the factors driving vegetation changes in subalpine areas is also not clear yet. We used linear regression and the Hurst exponent to analyze the trends in vegetation restoration and sustainability in different microtopography types since 2000, based on Fractional Vegetation Cover (FVC) and identified potential driving factors of vegetation change and their importance by using Geographical Detector. The results show that: (1) The FVC in the region under study has shown an up-trend since 2000, and the rate of increase is 0.26/year (

Indexed as

EcosystemSoilCold TemperatureHumansTemperatureTibetSoilDriving factorsEastern Tibet PlateauFVCSubalpineVegetation restoration

Identifiers

PMID35505680
PMCPMC9057294

What OpenQuestion holds

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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.