Why do plants lose water at night and how to model it at large scales

Optimization theory explains nighttime stomatal responses

Wang Y., Anderegg W. R. L., Venturas M. D., Trugman A. T., Yu K.,
Christian Frankenberg C. (2021)

Yujie Wang 1, William R. L. Anderegg 2, Martin D. Venturas 2, Anna T. Trugman 3, Kailiang Yu 4, Christian Frankenberg 1-5,

1 Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA;

2 School of Biological Sciences, University of Utah, Salt Lake City, UT 84112,
USA;

3 Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA;

4 Le Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCECEA/
CNRS/UVSQ Saclay, Gif-sur-Yvette 91191, France;

5 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA

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New Phytologist – doi: 10.1111/nph.17267 –

Summary

Nocturnal transpiration is widely observed across species and biomes, and may significantly impact global water, carbon, and energy budgets. However, it remains elusive why plants lose water at night and how to model it at large scales.
We hypothesized that plants optimize nighttime leaf diffusive conductance (gwn) to balance potential daytime photosynthetic benefits and nocturnal transpiration benefits. We quantified nighttime benefits from respiratory reductions due to evaporative leaf cooling. We described nighttime costs in terms of a reduced carbon gain during the day because of water use at night. We measured nighttime stomatal responses and tested our model with water birch (Betula occidentalis) saplings grown in a glasshouse.
The gwn of water birch decreased with drier soil, higher atmospheric CO2, wetter air, lower leaf temperature, and lower leaf respiration rate. Our model predicted all these responses correctly, except for the response of gwn to air humidity. Our results also suggested that the slow decrease in gwn after sunset could be associated with decreasing leaf respiration.
The optimality-based nocturnal transpiration model smoothly integrates with daytime stomatal optimization approaches, and thus has the potential to quantitatively predict nocturnal transpiration across space and time.