Evidence map›Paper›PMID 35707613›Full record

ArticleFrontiers in plant science2022

Unravelling Trait-Environment Relationships at Local and Regional Scales in Temperate Forests.

Rihan Da, Minhui Hao, Xuetao Qiao, Chunyu Zhang, Xiuhai Zhao

Abstract read
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Rihan DaResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China.
Minhui HaoResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China.
Xuetao QiaoResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China.
Chunyu ZhangResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China.
Xiuhai ZhaoResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the trait-environment relationships has been a core ecological research topic in the face of global climate change. However, the strength of trait-environment relationships at the local and regional scales in temperate forests remains poorly known. In this study, we investigated the local and regional scale forest plots of the natural broad-leaved temperate forest in northeastern China, to assess what extent community-level trait composition depends on environmental drivers across spatial scales. We measured five key functional traits (leaf area, specific leaf area, leaf carbon content, leaf nitrogen content, and wood density) of woody plant, and quantified functional compositions of communities by calculating the "specific" community-weighted mean (CWM) traits. The sum of squares decomposition method was used to quantify the relative contribution of intraspecific trait variation to total trait variation among communities. Multiple linear regression model was then used to explore the community-level trait-environment relationships. We found that (

Indexed as

community functional compositionenvironmental driversfunctional traitintraspecific variationspatial scaletemperate forest

Identifiers

PMID35707613
PMCPMC9189410

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.