Evidence map›Paper›PMID 29089543›Full record

ArticleScientific reports2017

Species traits and connectivity constrain stochastic community re-assembly.

Rebecca E Holt, Christopher J Brown, Thomas A Schlacher, Fran Sheldon, Stephen R Balcombe, Rod M Connolly

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. 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

6 authors.

Rebecca E HoltAustralian Rivers Institute, School of Environment, Griffith University, Gold Coast, Qld, 4222, Australia. r.e.holt@ibv.uio.no.ORCID 0000-0002-7567-5518
Christopher J BrownAustralian Rivers Institute, Griffith University, Nathan, Qld, 4111, Australia.ORCID 0000-0002-7271-4091
Thomas A SchlacherSchool of Science and Engineering, The University of the Sunshine Coast, Maroochydore, Qld, 4558, Australia.
Fran SheldonAustralian Rivers Institute, School of Environment, Griffith University, Nathan, Qld, 4111, Australia.
Stephen R BalcombeAustralian Rivers Institute, Griffith University, Nathan, Qld, 4111, Australia.
Rod M ConnollyAustralian Rivers Institute, School of Environment, Griffith University, Gold Coast, Qld, 4222, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

All communities may re-assemble after disturbance. Predictions for re-assembly outcomes are, however, rare. Here we model how fish communities in an extremely variable Australian desert river re-assemble following episodic floods and drying. We apply information entropy to quantify variability in re-assembly and the dichotomy between stochastic and deterministic community states. Species traits were the prime driver of community state: poor oxygen tolerance, low dispersal ability, and high fecundity constrain variation in re-assembly, shifting assemblages towards more stochastic states. In contrast, greater connectivity, while less influential than the measured traits, results in more deterministic states. Ecology has long recognised both the stochastic nature of some re-assembly trajectories and the role of evolutionary and bio-geographic processes. Our models explicitly test the addition of species traits and landscape linkages to improve predictions of community re-assembly, and will be useful in a range of different ecosystems.

Indexed as

AnimalsAustraliaBehavior, AnimalBiological EvolutionBiotaEcologyEcosystemFishesFloodsHydrobiologyInstinctModels, TheoreticalRiversStochastic Processes

Identifiers

PMID29089543
PMCPMC5663852

What OpenQuestion holds

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LicenceCC BY
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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.