Journal article
Species traits and connectivity constrain stochastic community re-assembly
Scientific Reports, Vol.7, pp.1-8
2017
PMCID: PMC5663852
PMID: 29089543
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.
Details
- Title
- Species traits and connectivity constrain stochastic community re-assembly
- Authors
- Rebecca E Holt (Author) - Griffith UniversityChristopher J Brown (Author) - Griffith UniversityThomas Schlacher (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and EngineeringFran Sheldon (Author) - Griffith UniversityStephen R Balcombe (Author) - Griffith UniversityRod M Connolly (Author) - Griffith University
- Publication details
- Scientific Reports, Vol.7, pp.1-8
- Publisher
- Nature Publishing Group
- Date published
- 2017
- DOI
- 10.1038/s41598-017-14774-2
- ISSN
- 2045-2322
- PMID
- 29089543; PMC5663852
- Copyright note
- Copyright © The Author(s) 2017. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Grants
- Organisation Unit
- School of Science and Engineering - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99450577802621
- Output Type
- Journal article
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