Evidence map›Paper›PMID 28839285›Full record

ArticleScientific reports2017

Metastability for discontinuous dynamical systems under Lévy noise: Case study on Amazonian Vegetation.

Larissa Serdukova, Yayun Zheng, Jinqiao Duan, Jürgen Kurths

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. 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. Review
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.

Larissa SerdukovaSchool of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Yayun ZhengSchool of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China. yayun2046@163.com.
Jinqiao DuanCenter for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, 430074, China.
Jürgen KurthsCenter for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For the tipping elements in the Earth's climate system, the most important issue to address is how stable is the desirable state against random perturbations. Extreme biotic and climatic events pose severe hazards to tropical rainforests. Their local effects are extremely stochastic and difficult to measure. Moreover, the direction and intensity of the response of forest trees to such perturbations are unknown, especially given the lack of efficient dynamical vegetation models to evaluate forest tree cover changes over time. In this study, we consider randomness in the mathematical modelling of forest trees by incorporating uncertainty through a stochastic differential equation. According to field-based evidence, the interactions between fires and droughts are a more direct mechanism that may describe sudden forest degradation in the south-eastern Amazon. In modeling the Amazonian vegetation system, we include symmetric α-stable Lévy perturbations. We report results of stability analysis of the metastable fertile forest state. We conclude that even a very slight threat to the forest state stability represents L´evy noise with large jumps of low intensity, that can be interpreted as a fire occurring in a non-drought year. During years of severe drought, high-intensity fires significantly accelerate the transition between a forest and savanna state.

Indexed as

Environmental ExposurePlant DispersalPopulation DynamicsBiostatisticsModels, TheoreticalRainforestTreesTropical Climate

Identifiers

PMID28839285
PMCPMC5571188

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.