Journal article
Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology
- Abstract:
- In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.7MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-023-28132-y
Authors
- Publisher:
- Springer Nature
- Journal:
- Scientific Reports More from this journal
- Volume:
- 13
- Issue:
- 1
- Article number:
- 2859
- Place of publication:
- England
- Publication date:
- 2023-02-17
- Acceptance date:
- 2023-01-13
- DOI:
- EISSN:
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2045-2322
- ISSN:
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2045-2322
- Pmid:
-
36801913
- Language:
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English
- Keywords:
- Pubs id:
-
1329576
- Local pid:
-
pubs:1329576
- Deposit date:
-
2023-11-01
Terms of use
- Copyright holder:
- Pos et al
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. Tis 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 licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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