Article ; Online: Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.
2023 Volume 13, Issue 1, Page(s) 2859
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 ...
| 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. |
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| MeSH term(s) | Ecosystem ; Entropy ; Biodiversity ; Forests ; Plants ; Ecology ; Tropical Climate |
| Language | English |
| Publishing date | 2023-02-17 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-023-28132-y |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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