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Reduced resilience as an early warning signal of forest mortality

Nature Climate Change volume 9, pages 880–885 (2019)Cite this article

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Abstract

Climate-induced forest mortality is being widely observed across the globe. Predicting forest mortality remains challenging because the physiological mechanisms causing mortality are not fully understood and empirical relations between climatology and mortality are subject to change. Here, we show that the temporal loss of resilience, a phenomenon often detected as a system approaches a tipping point, can be used as an early warning signal (EWS) to predict the likelihood of forest mortality directly from remotely sensed vegetation dynamics. We tested the proposed approach on data from Californian forests and found that the EWS can often be detected before reduced greenness, between 6 to 19 months before mortality. The EWS shows a species-specific relation with mortality, and is able to capture its spatio-temporal variations. These findings highlight the potential for such an EWS to predict forest mortality in the near-term.

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Fig. 1: An example of EWS detected using the DLM.
Fig. 2: Temporal trajectories of drought severity, mortality area and EWS area from 2005 to 2015.
Fig. 3: Exceedance probability of the lead time of the EWS.
Fig. 4: Species-specific relations of the area showing EWS with mortality during the period 2005–2015.
Fig. 5: Temporal estimation and prediction accuracies using EWS characteristics with lead times ranging from 0 to 12 months.
Fig. 6: Observed, estimated and predicted mortality intensity in 2009 and 2015.

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Data availability

All datasets used in this study are publicly available from the referenced sources.

Code availability

The source code for the Bayesian DLM usedto identify the EWS is available at https://github.com/YanlanLiu/early-warning-signal-DLM.

References

  1. Van Mantgem, P. J. et al. Widespread increase of tree mortality rates in the western United States. Science 323, 521–524 (2009).

    Article  CAS  Google Scholar 

  2. Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).

    Article  Google Scholar 

  3. Settele, J. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 271–359 (IPCC, Cambridge Univ. Press, 2015).

  4. McDowell, N. G. et al. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 200, 304–321 (2013).

    Article  CAS  Google Scholar 

  5. Parolari, A. J., Katul, G. G. & Porporato, A. An ecohydrological perspective on drought-induced forest mortality. J. Geophys. Res. Biogeosci. 119, 965–981 (2014).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).

    Article  CAS  Google Scholar 

  8. Anderegg, L. D. L., Anderegg, W. R. L., Abatzoglou, J., Hausladen, A. M. & Berry, J. A. Drought characteristics’ role in widespread aspen forest mortality across Colorado, USA. Glob. Change Biol. 19, 1526–1537 (2013).

    Article  Google Scholar 

  9. Anderegg, W. R. et al. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8, 367–371 (2015).

    Article  CAS  Google Scholar 

  10. McDowell, N. G. Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol. 155, 1051–1059 (2011).

    Article  CAS  Google Scholar 

  11. McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).

    Article  Google Scholar 

  12. Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352 (2016).

    Article  Google Scholar 

  13. Wolf, A., Anderegg, W. R. & Pacala, S. W. Optimal stomatal behavior with competition for water and risk of hydraulic impairment. Proc. Natl Acad. Sci. USA 113, E7222–E7230 (2016).

    Article  CAS  Google Scholar 

  14. Sala, A., Piper, F. & Hoch, G. Physiological mechanisms of drought-induced tree mortality are far from being resolved. New Phytol. 186, 274–281 (2010).

    Article  Google Scholar 

  15. Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).

    Article  CAS  Google Scholar 

  16. Young, D. J. et al. Long-term climate and competition explain forest mortality patterns under extreme drought. Ecol. Lett. 20, 78–86 (2017).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. van Nes, E. H. et al. What do you mean, ‘tipping point’? Trends Ecol. Evol. 31, 902–904 (2016).

    Article  Google Scholar 

  19. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    Article  CAS  Google Scholar 

  20. Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions. Oikos 122, 641–648 (2013).

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. 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 

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

    Article  CAS  Google Scholar 

  24. Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Phil. Trans. R. Soc. B 370, 20130263 (2015).

    Article  Google Scholar 

  25. Ives, A. R. & Dakos, V. Detecting dynamical changes in nonlinear time series using locally linear state-space models. Ecosphere 3, 1–15 (2012).

    Article  Google Scholar 

  26. Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7, e41010 (2012).

    Article  CAS  Google Scholar 

  27. US Forest Service Pacific Southwest Region Forest Health Protection Aerial Detection Survey (US Forest Service, accessed 25 September 2017); https://www.fs.usda.gov/detail/r5/forest-grasslandhealth

  28. Swann, A. L. et al. Continental-scale consequences of tree die-offs in North America: identifying where forest loss matters most. Environ. Res. Lett. 13, 055014 (2018).

    Article  Google Scholar 

  29. 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).

    Article  CAS  Google Scholar 

  30. Brodrick, P. & Asner, G. Remotely sensed predictors of conifer tree mortality during severe drought. Environ. Res. Lett. 12, 115013 (2017).

    Article  Google Scholar 

  31. Dai, A., Trenberth, K. E. & Qian, T. A global dataset of Palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130 (2004).

    Article  Google Scholar 

  32. Limousin, J.-M. et al. Morphological and phenological shoot plasticity in a Mediterranean evergreen oak facing long-term increased drought. Oecologia 169, 565–577 (2012).

    Article  Google Scholar 

  33. Gaylord, M. L. et al. Drought predisposes piñon-juniper woodlands to insect attacks and mortality. New Phytol. 198, 567–578 (2013).

    Article  CAS  Google Scholar 

  34. Mueller, R. C. et al. Differential tree mortality in response to severe drought: evidence for long-term vegetation shifts. J. Ecol. 93, 1085–1093 (2005).

    Article  Google Scholar 

  35. McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).

    Article  Google Scholar 

  36. Munné-Bosch, S. & Alegre, L. Die and let live: leaf senescence contributes to plant survival under drought stress. Funct. Plant Biol. 31, 203–216 (2004).

    Article  Google Scholar 

  37. Tai, X., Mackay, D. S., Anderegg, W. R., Sperry, J. S. & Brooks, P. D. Plant hydraulics improves and topography mediates prediction of aspen mortality in southwestern USA. New Phytol. 213, 113–127 (2017).

    Article  CAS  Google Scholar 

  38. Prado, R. & West, M. Time Series: Modeling, Computation, and Inference (CRC, 2010).

  39. 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).

    Article  CAS  Google Scholar 

  40. Novick, K., Katul, G., McCarthy, H. & Oren, R. Increased resin flow in mature pine trees growing under elevated CO2 and moderate soil fertility. Tree Physiol. 32, 752–763 (2012).

    Article  CAS  Google Scholar 

  41. 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).

    Article  CAS  Google Scholar 

  42. Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).

    Article  Google Scholar 

  43. Anderegg, W. R., Anderegg, L. D. & Huang, C.-y Testing early warning metrics for drought-induced tree physiological stress and mortality. Glob. Change Biol. 25, 2459–2469 (2019).

    Article  Google Scholar 

  44. Rogers, B. M. et al. Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data. Glob. Change Biol. 24, 2284–2304 (2018).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Walther, S. et al. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob. Change Biol. 22, 2979–2996 (2016).

    Article  Google Scholar 

  47. Churchill, D. J. et al. Restoring forest resilience: from reference spatial patterns to silvicultural prescriptions and monitoring. For. Ecol. Manage. 291, 442–457 (2013).

    Article  Google Scholar 

  48. Hessburg, P. F. et al. Tamm review: management of mixed-severity fire regime forests in Oregon, Washington, and Northern California. For. Ecol. Manage. 366, 221–250 (2016).

    Article  Google Scholar 

  49. Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).

    Article  CAS  Google Scholar 

  50. Landsat 7 ETM+ Surface Reflectance (US Geological Survey, accessed 21 July 2017); https://landsat.usgs.gov/landsat-surface-reflectance-data-products

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

    Article  CAS  Google Scholar 

  52. MODIS Active Fire Detections for the CONUS (2002–2015) (US Forest Service, accessed 13 September 2017); https://fsapps.nwcg.gov/afm/gisdata.php

  53. Thornton, P. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America Version 3 (Oak Ridge National Laboratory, accessed 15 September 2017); https://doi.org/10.3334/ORNLDAAC/1328

  54. Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).

    Article  Google Scholar 

  55. GNN Structure (Species-Size) Maps (LEMMA Group, accessed 4 December 2017); http://lemma.forestry.oregonstate.edu/data/structure-maps

  56. Existing Vegetation–CALVEG (US Forest Service, accessed 7 October 2017); https://www.fs.usda.gov/main/r5/landmanagement/gis

  57. West, M. & Harrison, J. Bayesian Forecasting and Dynamic Models (Springer, 1997).

  58. Finley, A., Banerjee, S. & Gelfand, A. spBayes for large univariate and multivariate point–referenced spatio-temporal data models. J. Stat. Softw. 63, 1–28 (2015).

    Article  Google Scholar 

  59. R Core Team R Version 3.4.3: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  60. Gelfand, A. E., Banerjee, S. & Gamerman, D. Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics 16, 465–479 (2005).

    Article  Google Scholar 

  61. Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian regression models. Am. Stat. 73, 307–309 (2018).

    Article  Google Scholar 

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Acknowledgements

We thank J.S. Clark, M. West and C. Rundel for discussions and insightful suggestions. M.K. acknowledges support from the National Science Foundation (NSF, grant nos. EAR-1454983 and EAR-1331846). G.K. acknowledges support from the NSF (grant nos. EAR-1344703, AGS-1644382, IOS-1754893 and DGE-1068871). A.P. acknowledges support from the NSF (grant nos. EAR-1331846, DGE-1068871 and EAR-1316258). The publication cost was shared by The University of Alabama–Alabama Water Institute.

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Authors and Affiliations

  1. Nicholas School of the Environment, Duke University, Durham, NC, USA

    Yanlan Liu, Mukesh Kumar & Gabriel G. Katul

  2. Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA

    Mukesh Kumar

  3. Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA

    Gabriel G. Katul

  4. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

    Amilcare Porporato

  5. Princeton Environmental Institute, Princeton University, Princeton, NJ, USA

    Amilcare Porporato

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  1. Yanlan Liu
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Contributions

Y.L. and M.K. conceived the study. Y.L. prepared data and performed the analysis. G.K. and A.P. further improved the physical basis and assumptions. All authors contributed to interpreting the results and writing the manuscript.

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Correspondence to Mukesh Kumar.

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Peer review information Nature Climate Change thanks Christopher Schwalm and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary notes, methods, discussion, Figs. 1–29, Tables 1–3 and references.

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Liu, Y., Kumar, M., Katul, G.G. et al. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Chang. 9, 880–885 (2019). https://doi.org/10.1038/s41558-019-0583-9

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