Evidence map›Paper›PMID 32028944›Full record

ArticleBMC ecology2020

Monitoring of the invasion of Spartina alterniflora from 1985 to 2015 in Zhejiang Province, China.

Nan Li, Longwei Li, Yinlong Zhang, Ming Wu

Abstract read
In one paragraph

Article in BMC ecology, 2020. 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

4 authors.

Nan LiCollaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing, 311300, China.ORCID 0000-0002-9260-3882
Longwei LiSchool of Environmental & Resource Sciences, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China. xflilongwei@126.com.ORCID 0000-0003-0847-5513
Yinlong ZhangCollaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing, 311300, China.
Ming WuInstitute of Subtropical Forestry Research, Hangzhou, 311400, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSpartina alterniflora is an invasive plant on the coast of China that replaces native vegetation and has a serious negative impact on local ecosystems. Monitoring the spatial distribution of S. alterniflora and its changes over time can reveal its expansion mechanism, which is crucial for the management of coastal ecosystems. The purpose of this study was to map the distribution of S. alterniflora in Zhejiang Province from 1985 to 2015 using a time series of Landsat TM/OLI images and analyze the temporal and spatial patterns of expansion of this species.

resultsAfter analyzing the distribution of coastal vegetation, the vegetation index was calculated based on Landsat images for 4 years (1985, 1995, 2005 and 2015). According to a threshold determined based on expert knowledge, the distribution of S. alterniflora in Zhejiang Province was extracted, and the temporal and spatial changes in the distribution of S. alterniflora were analyzed. The classification accuracy was 90.3%. S. alterniflora has expanded rapidly in recent decades after being introduced into southern Zhejiang. Between 1985 and 2015, S. alterniflora increased its area of distribution by 10,000 hm

conclusionsThis study reveals the changes over time in S. alterniflora cover in Zhejiang and can contribute to the control and management of this invasive plant.

Indexed as

EcosystemWetlandsChinaIntroduced SpeciesPoaceaeDynamic changeExpert knowledgeInvasive plantsLandsat imagesSpartina alterniflora

Identifiers

PMID32028944
PMCPMC7006405

What OpenQuestion holds

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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.