Evidence map›Paper›PMID 28842647›Full record

ArticleScientific reports2017

Spatially destabilising effect of woody plant diversity on forest productivity in a subtropical mountain forest.

Yonglin Zhong, Yudan Sun, Mingfeng Xu, Yi Zhang, Yongqiang Wang, Zhiyao Su

Abstract read
In one paragraph

Article in Scientific reports, 2017. 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

6 authors.

Yonglin ZhongCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
Yudan SunCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
Mingfeng XuCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
Yi ZhangCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
Yongqiang WangCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China.
Zhiyao SuCollege of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, China. zysu@scau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We used geographically weighted regression to investigate the relationship between biodiversity and the spatial stability of forest productivity (SSFP) in a subtropical mountain forest. We examined the effect of elevation on this relationship and on its spatial non-stationarity. We found that higher woody plant diversity reduced SSPF. Higher woody plant diversity strengthened the asynchrony of species responses to spatial heterogeneity of forest habitats, which contributed to SSFP, but reduced two factors that enhanced SSFP: species dominance and the spatial stability of the dominant species. The percentage of variation in SSFP explained by diversity measures was highest for the Shannon-Wiener index, lowest for functional dispersion, and intermediate for species richness. The correlations of woody plant diversity with SSFP became stronger with elevation and varied among plots, indicating that the spatial non-stationarity existed in the biodiversity-SSFP relationship. These correlations became weaker in most cases after controlling for elevation. Our results suggest that in the subtropical mountain forest higher woody plant diversity has a spatially destabilising effect on forest productivity, particularly at higher elevations.

Indexed as

BiodiversityEnvironmentForestsPlantsWoodAltitudeChinaTropical Climate

Identifiers

PMID28842647
PMCPMC5573360

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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.