Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).
IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).
Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).
Dai, A. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).
Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dyn. 43, 2607–2627 (2014).
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187 (2000).
Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).
Miao, S., Zou, C. B. & Breshears, D. D. Vegetation responses to extreme hydrological events: sequence matters. Am. Nat. 173, 113–118 (2008).
Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).
Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
Gunderson, L. H. et al. Ecological resilience—in theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).
Ingrisch, J. & Bahn, M. Towards a comparable quantification of resilience. Trends Ecol. Evol. 33, 251–259 (2018).
Bartlett, M. K. et al. Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol. Lett. 17, 1580–1590 (2014).
Martínez‐Vilalta, J. et al. Hydraulic adjustment of Scots pine across Europe. New Phytol. 184, 353–364 (2009).
Hacke, U. G., Stiller, V., Sperry, J. S., Pittermann, J. & McCulloh, K. A. Cavitation fatigue. Embolism and refilling cycles can weaken the cavitation resistance of xylem. Plant Physiol. 125, 779–786 (2001).
Sala, A., Woodruff, D. R. & Meinzer, F. C. Carbon dynamics in trees: feast or famine? Tree Physiol. 32, 764–775 (2012).
Schymanski, S. J., Roderick, M. L., Sivapalan, M., Hutley, L. B. & Beringer, J. A canopy-scale test of the optimal water-use hypothesis. Plant Cell Environ. 31, 97–111 (2008).
Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).
Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).
Royer, P. D. et al. Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: primary and secondary ecological implications. J. Ecol. 99, 714–723 (2011).
Raffa, K. F. et al. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience 58, 501–517 (2008).
Anderegg, W. R., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).
Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).
Camarero, J. J., Gazol, A., Sangüesa-Barreda, G., Oliva, J. & Vicente-Serrano, S. M. To die or not to die: early warnings of tree dieback in response to a severe drought. J. Ecol. 103, 44–57 (2015).
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).
Saatchi, S. et al. Persistent effects of a severe drought on Amazonian forest canopy. Proc. Natl Acad. Sci. USA 110, 565–570 (2013).
Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).
Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl Acad. Sci. USA 108, 1474–1478 (2011).
Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).
Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).
Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl Acad. Sci. USA 112, 13172–13177 (2015).
Johnson, D. M., McCulloh, K. A., Woodruff, D. R. & Meinzer, F. C. Hydraulic safety margins and embolism reversal in stems and leaves: why are conifers and angiosperms so different? Plant Sci. 196, 48–53 (2012).
Morris, H. et al. A global analysis of parenchyma tissue fractions in secondary xylem of seed plants. New Phytol. 209, 1553–1565 (2016).
DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 1–9 (2020).
Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).
Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).
Trugman, A. T. et al. Climate and plant trait strategies determine tree carbon allocation to leaves and mediate future forest productivity. Glob. Change Biol. 25, 3395–3405 (2019).
Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).
Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M. & El Kenawy, A. A new global 0.5 gridded dataset (1901–2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index. J. Hydrometeorol. 11, 1033–1043 (2010).
Beguería, S., Vicente-Serrano, S. M. & Angulo-Martínez, M. A multiscalar global drought dataset: the SPEIbase: a new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 91, 1351–1356 (2010).
Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized Precipitation Evapotranspiration Index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).
Gazol, A., Camarero, J. J., Anderegg, W. R. L. & Vicente-Serrano, S. M. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).
Klesse, S. et al. Sampling bias overestimates climate change impacts on forest growth in the southwestern United States. Nat. Commun. 9, 5336 (2018).
Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures General Technical Report SRS-80 (USDA, 2005).
Bechtold, W. & Scott, C. T. in The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures General Technical Report SRS-80 (eds Bechtold, W. A. & Patterson, P. L.) 37–52 (USDA, 2005).
Woudenberg, S. W. et al. The Forest Inventory and Analysis Database: Database Description and Users Manual Version 4.0 for Phase 2 General Technical Report RMRS-GTR-245 (USDA, 2010).
Jacobi, W. R., Kearns, H. S. J. & Johnson, D. W. Persistence of pinyon pine snags and logs in southwestern Colorado. West. J. Appl. For. 20, 247–252 (2005).
Shaw, J. D. et al. Arizona’s Forest Resources, 2001–2014 Resource Bulletin RMRS-RB-25 (USDA, 2018).
Shaw, J. D., Steed, B. E. & DeBlander, L. T. Forest inventory and analysis (FIA) annual inventory answers the question: what is happening to pinyon-juniper woodlands? J. For. 103, 280–285 (2005).
Breshears, D. D. et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl Acad. Sci. USA 102, 15144–15148 (2005).
Williams, A. P. et al. Forest responses to increasing aridity and warmth in the southwestern United States. Proc. Natl Acad. Sci. USA 107, 21289–21294 (2010).
Anderegg, W. R. et al. Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674–683 (2015).
Jackson, T. J. & Schmugge, T. J. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).
Tian, F. et al. Remote sensing of vegetation dynamics in drylands: evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).
Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).
Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeosci. 122, 3031–3046 (2017).
Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).
Van de Griend, A. A. & Wigneron, J.-P. The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans. Geosci. Remote Sens. 42, 786–794 (2004).
Konings, A. G., Rao, K. & Steele-Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. New Phytol. https://doi.org/10.1111/nph.15808 (2019).
Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave earth observations. Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2017-27 (2017).
Du, J., Kimball, J. S., Jones, L. A. & Member, S. Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E. IEEE Trans. Geosci. Remote Sens. 54, 597–608 (2015).
Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data 9, 791–808 (2017).
Jones, L. A. et al. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 3, 111–123 (2010).
Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).
Zar, J. H. in Biostatistical Analysis 1st edn, 185–205 (Prentice-Hall International, 1984).
Fox, J. et al. Package ‘car’ (R Foundation for Staistical Computing, 2012).
Fox, J., Friendly, M. & Weisberg, S. Hypothesis tests for multivariate linear models using the car package. R J. 5, 39–52 (2013).
Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).
Pinheiro, J. et al. nlme: linear and nonlinear mixed effects models. R package v.3.1-117 (R Foundation for Statistical Computing, 2014).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012).
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