ArticleProceedings of the National Academy of Sciences of the United States of America2025
Embolism resistance supports the contribution of dry-season precipitation to transpiration in eastern Amazon forests.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Physiological fidelity of a satellite-derived forest resilience indicator in the Amazon.Nature ecology & evolution · 2026Article
- Embolism resistance supports the contribution of dry-season precipitation to transpiration in eastern Amazon forests.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
Transpiration drives most of the local rainfall during the dry season in the Amazon forests by recycling moisture into the atmosphere. However, the source, temporal origin of transpiration, and spatial distribution of transpiration water sources remain unclear. Here, we quantify transpiration sources across a topographic gradient in the eastern Amazon. We show that on hills, dry-season transpiration sources are mostly shallow soil water recharged by dry-season rainfall. This is different in valleys, where tree water sources include both shallow and deep soil layers, with both dry- and wet-season contributions. We show that species embolism resistance largely explains this pattern in tree water use but with contrasting trade-offs between topographic positions. The significant relationship between embolism resistance and depth of water uptake in both hill and valley species may merit incorporation into process-based models to understand changes in vegetation and land surface fluxes.
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