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

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.

La forestería y su relación con el otro-que-humano

“…Cada elemento, cada proceso, cada flujo, cada dinámica no lineal del complejo sistema forestal de los bosques tropicales juega un rol fundamental para asegurar su continuidad. Interesa por tanto conocer los bosques en cuanto a la flora, la fauna, los microorganismos, la hidrología, la geología, la edafología, entre otras disciplinas, y sus diferentes interrelaciones (Pos et al, 2023;Harley et al, 2016;Araujo et al, 2011;Pallqui et al, 2014).…”