Evidence map›Paper›PMID 36699848›Full record

ArticleFrontiers in plant science2022

Middle to late Holocene plant cover variation in relation to climate, fire, and human activity in the Songnen grasslands of northeastern China.

Honghao Niu, Laurent Marquer, Dorothy Sack, Guizai Gao, Jiangyong Wang, Meng Meng, Dongmei Jie

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 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

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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

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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

7 authors.

Honghao NiuSchool of Geographical Sciences, Northeast Normal University, Changchun, China.
Laurent MarquerDepartment of Botany, University of Innsbruck, Innsbruck, Austria.
Dorothy SackDepartment of Geography, Ohio University, Athens, OH, United States.
Guizai GaoSchool of Geographical Sciences, Northeast Normal University, Changchun, China.
Jiangyong WangSchool of Geographical Sciences, Northeast Normal University, Changchun, China.
Meng MengSchool of Geographical Sciences, Northeast Normal University, Changchun, China.
Dongmei JieSchool of Geographical Sciences, Northeast Normal University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: For future vegetation projections and conservation planning in grassland ecosystems, accurate estimates of past plant cover changes in grassland composition and their responses to the various driving factors are essential. This study quantitatively reconstructs the past regional plant cover in the Songnen grasslands (northeastern China) and explores the relative importance of climate, fire, and human activity on vegetation dynamics. Methods: For this purpose, the Regional Estimates of Vegetation Abundance from Large Sites (REVEALS) model is applied to three pollen records from two areas, two in the center of the Songnen grasslands and one located in an area marginal to the grasslands. Results: Results from the most reliable REVEALS scenarios show that from the mid-Holocene, steppe (mean cover 40.6%) and dry steppe (mean cover 54.2%) alternately dominated the central part of the Songnen grasslands while the marginal grasslands were mainly characterized by alternating broadleaved forests (mean cover 26.3%), coniferous forests (mean cover 41.9%) and dry steppes (mean cover 30.1%). Discussion: By comparing the plant cover results with previous published regional climate, fire and human activity records, the results show that long term vegetation dynamics were mainly driven by East Asia Summer Monsoon (EASM) and the related precipitation variations, but was also affected by fire frequency and human activity. Moreover, vegetation evolution was sensitive to abrupt cooling events including the 4.2 ka BP and stacked ice-rafted debris (IRD) events; the change from steppe to dry steppe, for example, was driven by these abrupt climate changes. Fire events can alter the original vegetation stability allowing the vegetation to respond rapidly to climate changes while human activity merely has limited influence on vegetation changes.

Indexed as

4.2 ka BP eventEASMholocenepollenREVEALS (Regional Estimates of Vegetation Abundance from Large Sites) model

Identifiers

PMID36699848
PMCPMC9868567

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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.