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Summary Grasses are exceptionally productive, yet their hydraulic adaptation is paradoxical. Among C3grasses, a high photosynthetic rate (Aarea) may depend on higher vein density (Dv) and hydraulic conductance (Kleaf). However, the higherDvof C4grasses suggests a hydraulic surplus, given their reduced need for highKleafresulting from lower stomatal conductance (gs).Combining hydraulic and photosynthetic physiological data for diverse common garden C3and C4species with data for 332 species from the published literature, and mechanistic modeling, we validated a framework for linkages of photosynthesis with hydraulic transport, anatomy, and adaptation to aridity.C3and C4grasses had similarKleafin our common garden, but C4grasses had higherKleafthan C3species in our meta‐analysis. Variation inKleafdepended on outside‐xylem pathways. C4grasses have highKleaf : gs, which modeling shows is essential to achieve their photosynthetic advantage.Across C3grasses, higherAareawas associated with higherKleaf, and adaptation to aridity, whereas for C4species, adaptation to aridity was associated with higherKleaf : gs. These associations are consistent with adaptation for stress avoidance.Hydraulic traits are a critical element of evolutionary and ecological success in C3and C4grasses and are crucial avenues for crop design and ecological forecasting.
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TLSL e AF: automatic leaf angle estimates from single‐scan terrestrial laser scanning
Summary Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global‐scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open‐source method of deriving LADs from terrestrial laser scanning.TLSLeAF produces canopy‐scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf–wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community.TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer‐grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate toc. 25% andc. 11% increases in canopy net photosynthesis (c. 25%) and solar‐induced chlorophyll fluorescence (c. 11%).TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote‐sensing experts, allowing greater accessibility for addressing ecological hypotheses and large‐scale ecosystem modeling efforts.
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- Award ID(s):
- 2005574
- PAR ID:
- 10375264
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 232
- Issue:
- 4
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 1876-1892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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