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Empirical evidence for recent global shifts in vegetation resilience

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  • Verbesselt, J. et al. Remotely sensed resilience of tropical forests. Nat. Clim. Change 6, 1028–1031 (2016).

    Article 

    Google Scholar 

  • Lovejoy, T. E. & Nobre, C. Amazon tipping point. Sci. Adv. 4, eaat2340 (2018).

    Article 

    Google Scholar 

  • Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    CAS 
    Article 

    Google Scholar 

  • Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).

    CAS 
    Article 

    Google Scholar 

  • Ciemer, C. et al. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 12, 174–179 (2019).

    CAS 
    Article 

    Google Scholar 

  • Boers, N., Marwan, N. & Barbosa, H. M. J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 49, 41489 (2017).

    Article 
    CAS 

    Google Scholar 

  • Lasslop, G., Brovkin, V., Reick, C. H., Bathiany, S. & Kloster, S. Multiple stable states of tree cover in a global land surface model due to a fire–vegetation feedback. Geophys. Res. Lett. 43, 6324–6331 (2016).

    Article 

    Google Scholar 

  • Abis, B. & Brovkin, V. Environmental conditions for alternative tree-cover states in high latitudes. Biogeosciences 14, 511–527 (2017).

    CAS 
    Article 

    Google Scholar 

  • Bastiaansen, R. et al. Multistability of model and real dryland ecosystems through spatial self-organization. Proc. Natl Acad. Sci. USA 115, 11256–11261 (2018).

    CAS 
    Article 

    Google Scholar 

  • Lewis, S. L., Wheeler, C. E., Mitchard, E. T. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).

    CAS 
    Article 

    Google Scholar 

  • Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).

    Article 

    Google Scholar 

  • Folke, C. et al. Regime shifts, resilience, in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581 (2004).

    Article 

    Google Scholar 

  • Arani, B. M., Carpenter, S. R., Lahti, L., van Nes, E. H. & Scheffer, M. Exit time as a measure of ecological resilience. Science 372, eaay4895 (2021).

    CAS 
    Article 

    Google Scholar 

  • Einstein, A. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann. der Phys. 322, 549–560 (1905).

    Article 

    Google Scholar 

  • Nyquist, H. Thermal agitation of electric charge in conductors. Phys. Rev. 32, 110–113 (1928).

    CAS 
    Article 

    Google Scholar 

  • Kubo, R. The fluctuation–dissipation theorem. Rep. Prog. Phys. 29, 255–284 (1966).

    CAS 
    Article 

    Google Scholar 

  • Marconi, U. M. B., Puglisi, A., Rondoni, L. & Vulpiani, A. Fluctuation–dissipation: response theory in statistical physics. Phys. Rep. 461, 111–195 (2008).

    Article 

    Google Scholar 

  • Groth, A., Ghil, M., Hallegatte, S. & Dumas, P. The role of oscillatory modes in US business cycles. J. Bus. Cycle Meas. Anal. https://doi.org/10.1787/jbcma-2015-5jrs0lv715wl (2015).

  • Groth, A., Dumas, P., Ghil, M. & Hallegatte, S. in Extreme Events: Observations, Modeling, and Economics (eds Chavez, M. et al.) 343–360 (Wiley, 2015).

  • Gritsun, A. & Branstator, G. Climate response using a three-dimensional operator based on the fluctuation-dissipation theorem. J. Atmos. Sci. 64, 2558–2575 (2007).

    Article 

    Google Scholar 

  • Majda, A. J., Abramov, R. & Gershgorin, B. High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability. Proc. Natl Acad. Sci. USA 107, 581–586 (2010).

    CAS 
    Article 

    Google Scholar 

  • Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 311–318 (2006).

    CAS 
    Article 

    Google Scholar 

  • Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).

    CAS 
    Article 

    Google Scholar 

  • van der Bolt, B., van Nes, E. H., Bathiany, S., Vollebregt, M. E. & Scheffer, M. Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8, 478–484 (2018).

    Article 

    Google Scholar 

  • Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Change 9, 880–885 (2019).

    Article 

    Google Scholar 

  • Van Nes, E. H. & Scheffer, M. Slow recovery from perturbations as a generic indicator of a nearby catastrophic shift. Am. Nat. 169, 738–747 (2007).

    Article 

    Google Scholar 

  • Dakos, V., Van Nes, E. H., d’Odorico, P. & Scheffer, M. Robustness of variance and autocorrelation as indicators of critical slowing down. Ecology 93, 264–271 (2012).

    Article 

    Google Scholar 

  • Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).

    CAS 
    Article 

    Google Scholar 

  • Carpenter, S. R. et al. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332, 1079–1082 (2011).

    CAS 
    Article 

    Google Scholar 

  • Veraart, A. J. et al. Recovery rates reflect distance to a tipping point in a living system. Nature 481, 357–359 (2012).

    CAS 
    Article 

    Google Scholar 

  • Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).

    CAS 
    Article 

    Google Scholar 

  • Rypdal, M. Early-warning signals for the onsets of Greenland interstadials and the Younger Dryas-preboreal transition. J. Clim. 29, 4047–4056 (2016).

    Article 

    Google Scholar 

  • Boers, N. Early-warning signals for Dansgaard–Oeschger events in a high-resolution ice core record. Nat. Commun. 9, 2556 (2018).

  • Lenton, T. M., Livina, V. N., Dakos, V., van Nes, E. H. & Scheffer, M. Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness. Phil. Trans. R. Soc. A 370, 1185–204 (2012).

    CAS 
    Article 

    Google Scholar 

  • Boulton, C. A., Allison, L. C. & Lenton, T. M. Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model. Nat. Commun. 5, 5752 (2014).

    CAS 
    Article 

    Google Scholar 

  • De Keersmaecker, W. et al. How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Glob. Change Biol. 20, 2149–2161 (2014).

    Article 

    Google Scholar 

  • De Keersmaecker, W. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).

    Article 

    Google Scholar 

  • Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).

    Article 

    Google Scholar 

  • Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (vodca). Earth Syst. Sci. Data 12, 177–196 (2020).

    Article 

    Google Scholar 

  • Boulton, C. A., Lenton, T. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).

    Article 

    Google Scholar 

  • Feng, Y. et al. Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale. Commun. Earth Environ. 2, 88 (2021).

    Article 

    Google Scholar 

  • Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05 Deg Version 006 (NASA, 2015).

  • Wang, W., Chen, Y., Becker, S. & Liu, B. Linear trend detection in serially dependent hydrometeorological data based on a variance correction Spearman rho method. Water 7, 7045–7065 (2015).

    CAS 
    Article 

    Google Scholar 

  • Boulton, C. A., Good, P. & Lenton, T. M. Early warning signals of simulated Amazon rainforest dieback. Theor. Ecol. 6, 373–384 (2013).

    Article 

    Google Scholar 

  • Box, E. O., Holben, B. N. & Kalb, V. Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio 80, 71–89 (1989).

    Article 

    Google Scholar 

  • Liu, L., Zhang, Y., Wu, S., Li, S. & Qin, D. Water memory effects and their impacts on global vegetation productivity and resilience. Sci. Rep. 8, 2962 (2018).

    Article 
    CAS 

    Google Scholar 

  • Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    CAS 
    Article 

    Google Scholar 

  • Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS 
    Article 

    Google Scholar 

  • Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).

    Article 

    Google Scholar 

  • Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. Stl: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6, 3–73 (1990).

    Google Scholar 

  • Donner, R. et al. Spatial patterns of linear and nonparametric long-term trends in Baltic sea-level variability. Nonlinear Process. Geophys. 19, 95–111 (2012).

    Article 

    Google Scholar 

  • Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci. Adv. 4, e1701550 (2018).

    Article 

    Google Scholar 

  • Smith, T., Boers, N. & Traxl, D. Global vegetation resilience estimation. Zenodo https://doi.org/10.5281/zenodo.5816934 (2022).

  • Rousseau, D.-D. et al. (MIS3 & 2) millennial oscillations in Greenland dust and Eurasian aeolian records—a paleosol perspective. Quat. Sci. Rev. 196, 99–113 (2017).

    Article 

    Google Scholar 

  • Boulton, C. A. & Lenton, T. M. A new method for detecting abrupt shifts in time series. F1000Research 8, 746 (2019).

    Article 

    Google Scholar 

  • Savitzky, A. & Golay, M. J. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).

    CAS 
    Article 

    Google Scholar 

  • Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).

    Article 

    Google Scholar 

  • Djikstra, H. Nonlinear Climate Dynamics (Cambridge Univ. Press, 2013).

    Book 

    Google Scholar 

  • Kendall, M. G. Rank Correlation Methods (Griffin, 1948).


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