The Sensitivity of Evapotranspiration to Inter-Specific Plant Neighbor Interactions: Implications for Models
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Evapotranspiration (ET) is an important water loss flux in ecosystem water cycles, and quantifying the spatial and temporal variation of ET can improve ecohydrological models in arid ecosystems. Plant neighbor interactions may be a source of spatial and temporal variation in ET due to their effects on the above- and belowground microclimate and increased water demand for transpiration. Over longer timescales (annual to multiple years), adjustments in plant physiological traits may occur in response to neighbor environments, potentially affecting the transpiration (T) component of ET. We used a dynamic soil water model to assess the sensitivity of ET and T estimates to neighbor effects on soil moisture via competition for water, aboveground microclimate effects via canopy shading, and physiological adjustments (specifically, root distribution, stomatal behavior, and canopy leaf area). We focus on a common desert shrub (Larrea tridentata) under different inter-specific neighbor environments and precipitation regimes. Neighbors impacted T of Larrea by as much as 75% at the patch scale (plant and surrounding soil) and 30% at the stand scale. Annual T estimates were highly sensitive to changes in soil moisture associated with competition for water, and the inclusion of physiological adjustments to neighbor environments significantly impacted seasonal T. Plant neighbor interactions can significantly influence ET and soil moisture, and their inclusion in models can help explain spatial and temporal variation in water fluxes in arid ecosystems. Furthermore, physiological adjustments to neighbor environments may be an important source of variation to include in models that operate over seasonal timescales or in studies focused on plant responses to precipitation under climate change.
Keywordsaridity competition evapotranspiration facilitation plant–plant interactions soil water model
This project was supported through funding provided to KO from the School of Life Sciences at Arizona State University. We thank Erick Pierson for his assistance with writing code for the automation of HYDRUS1D runs. We thank Michael Fell, Jessica Guo, and Drew Peltier for helpful feedback.
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