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Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration

  • 1.

    Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).

    • CAS
    • Google Scholar
  • 2.

    Seager, R. et al. Projections of declining surface-water availability for the southwestern United States. Nat. Clim. Change 3, 482–486 (2013).

    • Google Scholar
  • 3.

    Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).

    • CAS
    • Google Scholar
  • 4.

    Trugman, A., Medvigy, D., Mankin, J. & Anderegg, W. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).

    • Google Scholar
  • 5.

    Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    • CAS
    • Google Scholar
  • 6.

    Konings, A., Williams, A. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–289 (2017).

    • CAS
    • Google Scholar
  • 7.

    Rigden, A. J. & Salvucci, G. D. Stomatal response to humidity and CO2 implicated in recent decline in US evaporation. Global Change Biol. 23, 1140–1151 (2017).

    • Google Scholar
  • 8.

    Mirfenderesgi, G. et al. Tree level hydrodynamic approach for resolving aboveground water storage and stomatal conductance and modeling the effects of tree hydraulic strategy. J. Geophys. Res. Biogeosci. 121, 1792–1813 (2016).

    • Google Scholar
  • 9.

    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

    • CAS
    • Google Scholar
  • 10.

    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    • CAS
    • Google Scholar
  • 11.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  • 12.

    Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Ann. Rev. Plant Biol. 40, 19–36 (1989).

    • Google Scholar
  • 13.

    Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).

    • CAS
    • Google Scholar
  • 14.

    Feng, X., Dawson, T. E., Ackerly, D. D., Santiago, L. S. & Thompson, S. E. Reconciling seasonal hydraulic risk and plant water use through probabilistic soil–plant dynamics. Global Change Biol. 23, 3758–3769 (2017).

    • Google Scholar
  • 15.

    Oleson, K. W. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) NCAR Technical Note NCAR/TN-503+STR (National Center for Atmospheric Research, 2013).

  • 16.

    Milly, P. C. et al. An enhanced model of land water and energy for global hydrologic and earth-system studies. J. Hydrometeorol. 15, 1739–1761 (2014).

    • Google Scholar
  • 17.

    Bonan, G., Williams, M., Fisher, R. & Oleson, K. Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum. Geosci. Model Dev. 7, 2193–2222 (2014).

    • Google Scholar
  • 18.

    Anderegg, W. R. et al. Plant water potential improves prediction of empirical stomatal models. PloS ONE 12, e0185481 (2017).

    • Google Scholar
  • 19.

    Anderegg, W. R. Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation. New Phytol. 205, 1008–1014 (2015).

    • Google Scholar
  • 20.

    Meinzer, F. C., McCulloh, K. A., Lachenbruch, B., Woodruff, D. R. & Johnson, D. M. The blind men and the elephant: the impact of context and scale in evaluating conflicts between plant hydraulic safety and efficiency. Oecologia 164, 287–296 (2010).

    • Google Scholar
  • 21.

    Katul, G. G., Palmroth, S. & Oren, R. Leaf stomatal responses to vapour pressure deficit under current and CO2-enriched atmosphere explained by the economics of gas exchange. Plant Cell Environ. 32, 968–979 (2009).

    • CAS
    • Google Scholar
  • 22.

    Manzoni, S. et al. Optimizing stomatal conductance for maximum carbon gain under water stress: a meta-analysis across plant functional types and climates. Funct. Ecol. 25, 456–467 (2011).

    • Google Scholar
  • 23.

    Mrad, A. et al. A dynamic optimality principle for water use strategies explains isohydric to anisohydric plant responses to drought. Front. For. Global Change 2, 49 (2019).

    • Google Scholar
  • 24.

    Oren, R. et al. Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 22, 1515–1526 (1999).

    • Google Scholar
  • 25.

    Mrad, A., Domec, J.-C., Huang, C.-W., Lens, F. & Katul, G. A network model links wood anatomy to xylem tissue hydraulic behaviour and vulnerability to cavitation. Plant Cell Environ. 41, 2718–2730 (2018).

    • CAS
    • Google Scholar
  • 26.

    Venturas, M. D., Sperry, J. S. & Hacke, U. G. Plant xylem hydraulics: what we understand, current research, and future challenges. J. Integr. Plant Biol. 59, 356–389 (2017).

    • Google Scholar
  • 27.

    Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).

    • CAS
    • Google Scholar
  • 28.

    Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Global Change Biol. 24, 35–54 (2018).

    • Google Scholar
  • 29.

    Eller, C. B. et al. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Phil. Trans. R. Soc. B 373, 20170315 (2018).

    • Google Scholar
  • 30.

    Kennedy, D. et al. Implementing plant hydraulics in the community land model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).

    • Google Scholar
  • 31.

    Liu, Y. et al. Increasing atmospheric humidity and CO2 concentration alleviate forest mortality risk. Proc. Natl Acad. Sci. USA 114, 9918–9923 (2017).

    • CAS
    • Google Scholar
  • 32.

    Katul, G., Manzoni, S., Palmroth, S. & Oren, R. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration. Ann. Bot. 105, 431–442 (2009).

    • Google Scholar
  • 33.

    Farquhar, G. D., Caemmerer, S. V. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    • CAS
    • Google Scholar
  • 34.

    Huang, C.-W. et al. The effect of plant water storage on water fluxes within the coupled soil–plant system. New Phytol. 213, 1093–1106 (2017).

    • CAS
    • Google Scholar
  • 35.

    Cowan, I. & Farquhar, G. Stomatal function in relation to leaf metabolism and environment. Symp. Soc. Exp. Biol. 31, 471–505 (1977).

    • CAS
    • Google Scholar
  • 36.

    Hari, P., Mäkelä, A., Korpilahti, E. & Holmberg, M. Optimal control of gas exchange. Tree Physiol. 2, 169–175 (1986).

    • Google Scholar
  • 37.

    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol. 17, 2134–2144 (2011).

    • Google Scholar
  • 38.

    Sperry, J. S. et al. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell Environ. 40, 816–830 (2017).

    • CAS
    • Google Scholar
  • 39.

    Manzoni, S., Vico, G., Porporato, A. & Katul, G. Biological constraints on water transport in the soil–plant–atmosphere system. Adv. Water Resourc. 51, 292–304 (2013).

    • Google Scholar
  • 40.

    Clapp, R. B. & Hornberger, G. M. Empirical equations for some soil hydraulic properties. Water Resourc. Res. 14, 601–604 (1978).

    • Google Scholar
  • 41.

    Katul, G., Leuning, R. & Oren, R. Relationship between plant hydraulic and biochemical properties derived from a steady–state coupled water and carbon transport model. Plant Cell Environ. 26, 339–350 (2003).

    • CAS
    • Google Scholar
  • 42.

    FLUXNET 2015 Tier 1 Dataset (FLUXNET, accessed 25 July 2018); http://fluxnet.fluxdata.org/data/fluxnet2015-dataset

  • 43.

    Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, accessed 21 January 2019); https://doi.org/10.5067/MODIS/MCD15A3H.006

  • 44.

    Ukkola, A. M., Haughton, N., Kauwe, M. G. D., Abramowitz, G. & Pitman, A. J. FluxnetLSM R package (v1. 0): a community tool for processing FLUXNET data for use in land surface modelling. Geosci. Model Develop. 10, 3379–3390 (2017).

    • CAS
    • Google Scholar
  • 45.

    Healey, S. et al. CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004–2008 (ORNL DAAC, accessed 21 January 2019); https://doi.org/10.3334/ORNLDAAC/1271

  • 46.

    Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    • CAS
    • Google Scholar
  • 47.

    Jackson, R. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).

    • CAS
    • Google Scholar
  • 48.

    Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).

    • Google Scholar
  • 49.

    Harmonized World Soil Database Version 1.2 (FAO, accessed 22 June 2016); http://www.fao.org/soils-portal

  • 50.

    Thompson, S. E. et al. Comparative hydrology across AmeriFlux sites: the variable roles of climate, vegetation, and groundwater. Water Resourc. Res. 47, W00J07 (2011).

    • Google Scholar
  • 51.

    Kattge, J. et al. TRY—a global database of plant traits. Global Change Biol. 17, 2905–2935 (2011).

    • Google Scholar
  • 52.

    Martin-StPaul, N., Delzon, S. & Cochard, H. Plant resistance to drought depends on timely stomatal closure. Ecol. Lett. 20, 1437–1447 (2017).

    • Google Scholar
  • 53.

    Ji, C. & Schmidler, S. C. Adaptive Markov Chain Monte Carlo for Bayesian variable selection. J. Comput. Graph. Stat. 22, 708–728 (2013).

    • Google Scholar
  • 54.

    Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Graph. Stat. 7, 434–455 (1998).

    • Google Scholar

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