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Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

Pos, Edwin ; de Souza Coelho, Luiz; de Andrade Lima Filho, Diogenes; Salomão, Rafael P.; Amaral, Iêda Leão; Almeida Matos, Francisca Dionízia De; Castilho, Carolina V.; Phillips, Oliver L.; Guevara, Juan Ernesto; de Jesus Veiga Carim, Marcelo; López, Dairon Cárdenas; Magnusson, William E.; Wittmann, Florian 1; Irume, Mariana Victória; Martins, Maria Pires; Sabatier, Daniel; da Silva Guimarães, José Renan; Molino, Jean-François; Bánki, Olaf S.; ... mehr

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.


Verlagsausgabe §
DOI: 10.5445/IR/1000156335
Veröffentlicht am 03.03.2023
Originalveröffentlichung
DOI: 10.1038/s41598-023-28132-y
Scopus
Zitationen: 4
Web of Science
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2045-2322
KITopen-ID: 1000156335
Erschienen in Scientific Reports
Verlag Nature Research
Band 13
Seiten Art.-Nr.: 2859
Vorab online veröffentlicht am 17.02.2023
Schlagwörter Computational biology and bioinformatics, Ecology, Statistical physics, thermodynamics and nonlinear dynamics
Nachgewiesen in Web of Science
Scopus
Dimensions
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