Tree species explain only half of explained spatial variability in plant water sensitivity

Spatio-temporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically-driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Methods
The plant water sensitivity (PWS) data were calculated at 4 km resolution based on remote sensing-derived estimates of live fuel moisture content. The live fuel moisture content data, in turn, were retrieved according to the algorithm developed in the article:

Rao, K., Williams, A. P., Flefil, J. F., & Konings, A. G. (2020). SAR-enhanced mapping of live fuel moisture content. Remote Sensing of Environment, 245, 111797.

The PWS was then calculated based on the lagged relationship between live fuel moisture content and 4 km dead fuel moisture content.

We calculated the locations of all Forest Inventory and Assessment sites where at least 75% of the basal area is from a single species, and sampled the PWS at the 4 km pixel that contained each of these FIA sites. We then built a random forest model to predict PWS based on ten climatic, vegetation density, soil, and topographic predictors and compared the explanatory power of that random forest model to a model that predicts PWS based on the species-mean PWS of the dominant species at that site.