1. Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.
- Author
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Pos E, de Souza Coelho L, de Andrade Lima Filho D, Salomão RP, Amaral IL, de Almeida Matos FD, Castilho CV, Phillips OL, Guevara JE, de Jesus Veiga Carim M, López DC, Magnusson WE, Wittmann F, Irume MV, Martins MP, Sabatier D, da Silva Guimarães JR, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Mendoza AM, Ramos JF, Hawes JE, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, de Leão Novo EMM, Vargas PN, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Coronado ENH, Montero JC, Marimon BS, Marimon-Junior BH, Feldpausch TR, Duque A, Baraloto C, Arboleda NC, Engel J, Petronelli P, Zartman CE, Killeen TJ, Vasquez R, Mostacedo B, Assis RL, Schöngart J, Castellanos H, de Medeiros MB, Simon MF, Andrade A, Camargo JL, Demarchi LO, Laurance WF, Laurance SGW, de Sousa Farias E, Lopes MA, Magalhães JLL, Nascimento HEM, de Queiroz HL, Aymard GAC, Brienen R, Revilla JDC, Costa FRC, Quaresma A, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Ferreira LV, Comiskey JA, Draper F, de Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Lopes A, Vicentini A, Noronha JC, Barbosa FR, de Sá Carpanedo R, Emilio T, Levis C, de Jesus Rodrigues D, Schietti J, Souza P, Alonso A, Dallmeier F, Gomes VHF, Lloyd J, Neill D, de Aguiar DPP, Araujo-Murakami A, Arroyo L, Carvalho FA, de Souza FC, do Amaral DD, Feeley KJ, Gribel R, Pansonato MP, Barlow J, Berenguer E, Ferreira J, Fine PVA, Guedes MC, Jimenez EM, Licona JC, Mora MCP, Peres CA, Zegarra BEV, Cerón C, Henkel TW, Maas P, Silveira M, Stropp J, Thomas-Caesar R, Baker TR, Daly D, Dexter KG, Householder JE, Huamantupa-Chuquimaco I, Pennington T, Paredes MR, Fuentes A, Pena JLM, Silman MR, Tello JS, Chave J, Valverde FC, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Barbosa EM, de Matos Bonates LC, Doza HPD, Fonty É, Gómez RZ, Gonzales T, Gonzales GPG, Guillaumet JL, Hoffman B, Junqueira AB, Malhi Y, de Andrade Miranda IP, Pinto LFM, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Vos VA, Zent EL, Zent S, Albuquerque BW, Cano A, Correa DF, Costa JBP, Flores BM, Holmgren M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Torre EV, Vriesendorp C, Wang O, Young KR, Reategui MAA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Torres-Lezama A, Giraldo LEU, Villarroel D, Zagt R, Alexiades MN, Garcia-Cabrera K, Hernandez L, Milliken W, Cuenca WP, Pansini S, Pauletto D, Arevalo FR, Sampaio AF, Sandoval EHV, Gamarra LV, Boenisch G, Kattge J, Kraft N, Levesley A, Melgaço K, Pickavance G, Poorter L, and Ter Steege H
- Subjects
- Entropy, Forests, Plants, Ecology, Tropical Climate, Ecosystem, Biodiversity
- 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., (© 2023. The Author(s).)
- Published
- 2023
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