DOI: 10.5061/dryad.g1jwstr05
title: Data from: Tree species explain only half of explained spatial variability in plant water sensitivity

description: 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.
description: 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.
 <em>Remote Sensing of Environment</em>,
 <em>245</em>, 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.
description: # Data for: Tree species explain only half of explained spatial
 variability in plant water sensitivity
 [https://doi.org/10.5061/dryad.g1jwstr05](https://doi.org/10.5061/dryad.g1jwstr05) This dataset contains the inputs for, and outputs of, an analysis that compares how well an integrated metric of 'plant water sensitivity' can be explained by species type versus biogeographic factors. The plant water sensitivity metric is based on remotely sensed vegetation live fuel moisture content, and a map of this quantity across the Western United States is included. Also included are the maps of the biogeographic factors used to predict plant water sensitivity, as well as the predictions from both the biogeography and species-based models. ## Description of the data and file structure Two files are included in this dataset. These include: 1\) PWS.tiff: a GeoTIFF file that maps the plant water sensitivity (PWS) across the Western United States at 4 km 2\) PWS_species.nc: This netCDF file contains several variables that each have 21,455 values, corresponding to the 21,455 FIA locations studied in the analysis. The valuables include the land cover of each site, the dominant species at each site, the latitude and longitude of each site, the data-driven 'actual' plant water sensitivity at each site, the input features of the random forest model, and the predicted plant water sensitivity from the random forest model and the species based model The PWS_species.nc file was converted from a Python pandas dataset. The ordering of the sites (e.g. each of the 21,455) has no relevant meaning. ## Code/Software The Python code used to generate these files is available at [https://github.com/agkonings/PWSSpeciesAnalysis/](https://github.com/agkonings/PWSSpeciesAnalysis/) ## Manuscript citation Konings, A.G., Rao, K., McCormick, E.L et al (2024). Tree species explain only half of explained spatial variability in plant water sensitivity. Global Change Biology.