Paper

The Maximum Entropy Formalism of statistical mechanics in a biological application: a quantitative analysis of tropical forest ecology

Correa, Diego F, Stropp, Juliana, Bruno Barcante Ladvocat Cintra, Sabatier, Daniel, Mogollon, Hugo F, Euridice N Honorio Coronado, Cardenas Revilla, Juan David, Hoffman, Bruce, Magnusson, William E, Vasquez, Rodolfo, Marcelino Carneiro Guedes, Peres, Carlos A, Tello, J Sebastian, Zent, Eglee L, Gribel, Rogerio, Banki, Olaf S, Gonzales, Therany, Rocha, Maira, Pennington, Toby, Marcia Cleia Vilela Dos Santos, Camargo, Jose Luis, Janaina Costa Noronha, Pansonato, Marcelo Petratti, Luiz Carlos De Matos Bonates, Zent, Stanford, Abel Monteagudo Mendoza, Costa, Flavia Rc, Ben Hur Marimon-Junior, Helder Lima De Queiroz, Andre Braga Junqueira, Pauletto, Daniela, Fonty, Emile, Phillips, Oliver L, Nigel Ca Pitman, Alexiades, Miguel N, Petronelli, Pascal, Ferreira, Cid, Silveira, Marcos, Guevara, Juan Ernesto, Dairon Cardenas Lopez, Andrade, Ana, Marcelo Fragomeni Simon, Casula, Katia Regina, Dexter, Kyle G, Geertje Van Der Heijden, Mendoza, Casimiro, Barlow, Jos, Rivas-Torres, Gonzalo, Householder, John Ethan, Maas, Paul, Gomes, Vitor Hf, Vriesendorp, Corine, Hilario, Renato Richard, De Souza Coelho,

2021

Overview

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 almost ten times more of local relative abundances then constraints based on either directional or stabilizing 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. Competing Interest Statement The authors have declared no competing interest.

• Publisher: Cold Spring Harbor Laboratory Press
• Subject: Entropy / Environmental effects / Forest management / Geographical distribution / Quantitative analysis / Tropical forests