Evidence map›Paper›PMID 41068100›Full record

ArticleNature communications2025

Global loss of mountain vegetated landscapes and its impact on biodiversity conservation.

Chao Yang, Haiying Xu, Qingquan Li, Xuqing Wang, Bohui Tang, Junyi Chen, Wei Tu, Yinghui Zhang, Tiezhu Shi, Min Chen and 3 more

Abstract read
In one paragraph

Article in Nature communications, 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

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

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

13 authors.

Chao YangMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China.ORCID http://orcid.org/0000-0001-7724-0385
Haiying XuMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China.
Qingquan LiMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China. liqq@szu.edu.cn.ORCID http://orcid.org/0000-0002-2438-6046
Xuqing WangCenter for Hydrogeology and Environmental Geology, China Geological Survey, Baoding, China.
Bohui TangFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, China.
Junyi ChenFaculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming, China.ORCID http://orcid.org/0000-0001-8428-9259
Wei TuMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China.ORCID http://orcid.org/0000-0002-0255-4037
Yinghui ZhangMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China.
Tiezhu ShiMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China.
Min ChenKey Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China.ORCID http://orcid.org/0000-0001-8922-8789
Wei MaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing, China.
Huizeng LiuInstitute for Advanced Study & Shenzhen University, Shenzhen, China.ORCID http://orcid.org/0000-0002-9018-985X
Jonathan M ChaseGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.ORCID http://orcid.org/0000-0001-5580-4303

Funding

National Natural Science Foundation of China (National Science Foundation of China) No. 42471312, No. 42201319
6 · The paper itself

Abstract

Accelerating human encroachment and natural disasters are causing substantial loss of mountain vegetated landscapes, threatening biodiversity conservation and ecosystem sustainability. The global-scale quantification of the magnitude, variability and drivers of the loss of mountain vegetated landscapes, and its impact on biodiversity conservation, however, has been lacking. Here, we combine global datasets on mountain boundaries, land use, natural disasters, and protected and biodiversity hotspots together with large-scale earth observation data to quantify global mountain vegetated landscape loss, as well as its variation and potential drivers from 2000 to 2020. Overall, we find widespread but uneven mountain vegetated landscape loss across the globe, of which ∼89% can be attributed to human expansion, primarily agriculture, with small contributions of human settlement growth and mining. About ∼11% of mountain vegetated landscape loss can be attributed to natural disasters, primarily through drought. We also observe that ~56% of global mountain vegetated landscape loss occurred within protected areas and in areas with high richness of threatened mountain-occurring species, indicating the urgency of improving protection in these areas of loss. Our results can help formulate conservation strategies and contribute to sustainable development.

Indexed as

BiodiversityConservation of Natural ResourcesAgricultureEcosystemEndangered SpeciesHumansNatural Disasters

Identifiers

PMID41068100
PMCPMC12511303

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.