Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 02 2023
17 02 2023
Historique:
received:
11
03
2021
accepted:
13
01
2023
entrez:
21
2
2023
pubmed:
22
2
2023
medline:
25
2
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36801913
doi: 10.1038/s41598-023-28132-y
pii: 10.1038/s41598-023-28132-y
pmc: PMC9938116
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
2859Informations de copyright
© 2023. The Author(s).
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