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Multidecadal records of intrinsic water-use efficiency in the desert shrub Encelia farinosa reveal strong responses to climate change.

Avery W DriscollNicholas Q BitterDarren R SandquistJames R Ehleringer
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
While tree rings have enabled interannual examination of the influence of climate on trees, this is not possible for most shrubs. Here, we leverage a multidecadal record of annual foliar carbon isotope ratio collections coupled with 39 y of survey data from two populations of the drought-deciduous desert shrub Encelia farinosa to provide insight into water-use dynamics and climate. This carbon isotope record provides a unique opportunity to examine the response of desert shrubs to increasing temperature and water stress in a region where climate is changing rapidly. Population mean carbon isotope ratios fluctuated predictably in response to interannual variations in temperature, vapor pressure deficit, and precipitation, and responses were similar among individuals. We leveraged the well-established relationships between leaf carbon isotope ratios and the ratio of intracellular to ambient CO2 concentrations to calculate intrinsic water-use efficiency (iWUE) of the plants and to quantify plant responses to long-term environmental change. The population mean iWUE value increased by 53 to 58% over the study period, much more than the 20 to 30% increase that has been measured in forests [J. Peñuelas, J. G. Canadell, R. Ogaya, Glob. Ecol. Biogeogr. 20, 597-608 (2011)]. Changes were associated with both increased CO2 concentration and increased water stress. Individuals whose lifetimes spanned the entire study period exhibited increases in iWUE that were very similar to the population mean, suggesting that there was significant plasticity within individuals rather than selection at the population scale.
Keyphrases
  • climate change
  • human health
  • gas chromatography
  • air pollution
  • machine learning
  • genome wide
  • stress induced
  • particulate matter
  • electronic health record
  • high resolution
  • deep learning