Evidence map›Paper›PMID 34117123›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach.

Li Xu, Denis Patterson, Ann Carla Staver, Simon Asher Levin, Jin Wang

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Non-equilibrium early-warning signals for critical transitions in ecological systems.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  9. 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

5 authors.

Li XuState Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, People's Republic of China.ORCID 0000-0002-4714-1279
Denis PattersonHigh Meadows Environmental Institute, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-4509-7294
Ann Carla StaverDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520.ORCID 0000-0002-2384-675X
Simon Asher LevinDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544; slevin@princeton.edu jin.wang.1@stonybrook.edu.ORCID 0000-0002-8216-5639
Jin WangDepartment of Chemistry, Physics and Applied Mathematics, State University of New York at Stony Brook, Stony Brook, NY 11794-3400 slevin@princeton.edu jin.wang.1@stonybrook.edu.ORCID 0000-0002-2841-4913

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The frequency distributions can characterize the population-potential landscape related to the stability of ecological states. We illustrate the practical utility of this approach by analyzing a forest-savanna model. Savanna and forest states coexist under certain conditions, consistent with past theoretical work and empirical observations. However, a grassland state, unseen in the corresponding deterministic model, emerges as an alternative quasi-stable state under fluctuations, providing a theoretical basis for the appearance of widespread grasslands in some empirical analyses. The ecological dynamics are determined by both the population-potential landscape gradient and the steady-state probability flux. The flux quantifies the net input/output to the ecological system and therefore the degree of nonequilibriumness. Landscape and flux together determine the transitions between stable states characterized by dominant paths and switching rates. The intrinsic potential landscape admits a Lyapunov function, which provides a quantitative measure of global stability. We find that the average flux, entropy production rate, and free energy have significant changes near bifurcations under both finite and zero fluctuation. These may provide both dynamical and thermodynamic origins of the bifurcations. We identified the variances in observed frequency time traces, fluctuations, and time irreversibility as kinematic measures for bifurcations. This framework opens the way to characterize ecological systems globally, to uncover how they change among states, and to quantify the emergence of quasi-stable states under stochastic fluctuations.

Indexed as

Ecological and Environmental PhenomenaStochastic ProcessesEcosystemEntropyKineticsPoaceaeThermodynamicsTreesecological systemfluxglobal stabilitylandscape

Identifiers

PMID34117123
PMCPMC8214705

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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.