Publications by authors named "Demetrius Lira-Martins"

Premise: Plants survive in habitats with limited resource availability and contrasting environments by responding to variation in environmental factors through morphophysiological traits related to species performance in different ecosystems. However, how different plant strategies influence the megadiversity of tropical species has remained a knowledge gap.

Methods: We analyzed variations in 27 morphophysiological traits of leaves and secondary xylem in Erythroxylum pulchrum and Tapirira guianensis, which have the highest absolute dominance in these physiognomies and occur together in areas of restinga and dense ombrophilous forest to infer water-transport strategies of Atlantic Forest woody plants.

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Tropical trees store a large amount of nutrients in their woody tissues, thus triggering the question of what the functional association of these elements with other wood traits is. Given the osmotic activity of mineral elements such as potassium, sodium, and calcium, these elements should be strong candidates in mediating the water storing capacity in tropical trees. We investigated the role of wood nutrients in facilitating wood water storage in trees by using branch samples from 48 tropical tree species in South America and examined their associations with wood density (ρ).

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Bivariate relationships between plant tissue nutrient concentration have largely been studied across broad environmental scales regardless of their covariation with soil and climate. Comparing leaf and branch wood concentrations of C, Ca, K, Mg, N, Na, and P for trees growing in tropical forests in Amazonia and Australia we found that the concentrations of most elements varied with sampling location, but with foliar and branch woody tissues varying from site to site in different ways. Using a Mixed Effect Model (MEM) approach it was further found that relationships between branch and leaf concentrations within individual plots differed in terms of both slope and/or significance to the ordinary least squares (OLS) estimates for most elements.

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