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

0106 biological sciences Topography Rainforest Ecosystem Resilience Physiology Vulnerability Environmental Indicator Species Difference functional ecology 01 natural sciences Angiosperm Magnoliophyta Trees Amazonia Species Specificity Xylem Water Table Drought Resistance Tropical Forest Water Availability forest resilience phosphorus Phylogeny hydrological niches tropical forests Forest Ecosystem Environmental Gradient drought vulnerability Water Phosphorus P 15. Life on land 6. Clean water Spatial Distribution 13. Climate action Functional Change Coexistence Tree water table
DOI: 10.1111/nph.15463 Publication Date: 2018-10-08T15:47:34Z
ABSTRACT
Summary 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.
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