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Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro‐topographic gradients

Author:
Oliveira, Rafael S., Costa, Flavia R. C., van Baalen, Emma, de Jonge, Arjen, Bittencourt, Paulo R., Almanza, Yanina, Barros, Fernanda de V., Cordoba, Edher C., Fagundes, Marina V., Garcia, Sabrina, Guimaraes, Zilza T. M., Hertel, Mariana, Schietti, Juliana, Rodrigues‐Souza, Jefferson, Poorter, Lourens
Source:
Thenew phytologist 2019 v.221 no.3 pp. 1457-1465
ISSN:
0028-646X
Subject:
Angiospermae, biogeography, climate change, drought, embolism, evolutionary adaptation, highlands, models, nutrients, population distribution, prediction, rain forests, soil properties, stress tolerance, topography, trees, valleys, variance, water table, xylem
Abstract:
Species distribution is strongly driven by local and global gradients in water availability but the underlying mechanisms are not clear. Vulnerability to xylem embolism (P₅₀) is a key trait that indicates how species cope with drought and might explain plant distribution patterns across environmental gradients. Here we address its role on species sorting along a hydro‐topographical gradient in a central Amazonian rainforest and examine its variance at the community scale. We measured P₅₀ for 28 tree species, soil properties and estimated the hydrological niche of each species using an indicator of distance to the water table (HAND). We found a large hydraulic diversity, covering as much as 44% of the global angiosperm variation in P₅₀. We show that P₅₀: contributes to species segregation across a hydro‐topographic gradient in the Amazon, and thus to species coexistence; is the result of repeated evolutionary adaptation within closely related taxa; is associated with species tolerance to P‐poor soils, suggesting the evolution of a stress‐tolerance syndrome to nutrients and drought; and is higher for trees in the valleys than uplands. The large observed hydraulic diversity and its association with topography has important implications for modelling and predicting forest and species resilience to climate change.
Agid:
6277122