Abstract
Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPDk, where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or kDynamic, which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and kDynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, kDynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that kDynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.
| Original language | English (US) |
|---|---|
| Article number | 110996 |
| Journal | Agricultural and Forest Meteorology |
| Volume | 378 |
| DOIs | |
| State | Published - Mar 1 2026 |
Keywords
- Carbon gain
- Drylands
- Optimality
- Optimality constant
- Plant water-use
- Suboptimal
- Water use efficiency
ASJC Scopus subject areas
- Forestry
- Global and Planetary Change
- Agronomy and Crop Science
- Atmospheric Science
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