Edwin PosLuiz de Souza CoêlhoDiógenes de Andrade Lima FilhoRafael P. SalomãoIêda Leão do AmaralFrancisca Dionízia de Almeida MatosCarolina V. CastilhoOliver L. PhillipsJuan Ernesto GuevaraMarcelo de Jesus Veiga Carim2026-03-222026-03-22202110.1101/2021.03.31.437717https://doi.org/10.1101/2021.03.31.437717https://andeanlibrary.org/handle/123456789/83992Abstract 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.enPrinciple of maximum entropyEcologyRelative species abundanceAmazonianRelative abundance distributionEntropy maximizationAbundance (ecology)InferenceGeographyAmazon rainforestpreprint