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; de Almeida Matos, Francisca Dionízia; Castilho, Carolina V.; Phillips, Oliver L.; Guevara, Juan Ernesto; de Jesus Veiga Carim, Marcelo; López, Dairon Cárdenas; Magnusson, William E.; Wittmann, Florian; Irume, Mariana Victória; Martins, Maria Pires; Sabatier, Daniel; da Silva Guimarães, José Renan; Molino, Jean François; Bánki, Olaf S.; Piedade, Maria Teresa Fernandez; Pitman, Nigel C. A.; Mendoza, Abel Monteagudo; Ramos, José Ferreira; Hawes, Joseph E.; Almeida, Everton José; Barbosa, Luciane Ferreira; Cavalheiro, Larissa; dos Santos, Márcia Cléia Vilela; Luize, Bruno Garcia; de Leão Novo, Evlyn Márcia Moraes; Vargas, Percy Núñez; Silva, Thiago Sanna Freire; Venticinque, Eduardo Martins; Manzatto, Angelo Gilberto; Reis, Neidiane Farias Costa; Terborgh, John; Casula, Katia Regina; Coronado, Euridice N. Honorio; Montero, Juan Carlos; Marimon, Beatriz S.; Marimon-Junior, Ben Hur; Fel...

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.

Citation

Pos, E., de Souza Coelho, L., de Andrade Lima Filho, D., Salomão, R. P., Amaral, I. L., de Almeida Matos, F. D., …ter Steege, H. (2023). Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology. Scientific Reports, 13, Article 2859. https://doi.org/10.1038/s41598-023-28132-y

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2023
• Online Publication Date: Feb 17, 2023
• Publication Date: Feb 17, 2023
• Deposit Date: Mar 7, 2023
• Publicly Available Date: Mar 9, 2023
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Article Number: 2859
• DOI: https://doi.org/10.1038/s41598-023-28132-y
• Public URL: https://nottingham-repository.worktribe.com/output/17651987
• Publisher URL: https://www.nature.com/articles/s41598-023-28132-y
• Additional Information: Received: 11 March 2021; Accepted: 13 January 2023; First Online: 17 February 2023; : The authors declare no competing interests.