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Climate-driven disturbances amplify forest drought sensitivity

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Abstract

Forests are a major terrestrial carbon sink, but the increasing frequency and intensity of climate-driven disturbances such as droughts, fires and biotic agent outbreaks is threatening carbon uptake and sequestration. Determining how climate-driven disturbances may alter the capacity of forest carbon sinks in a changing climate is crucial. Here we show that the sensitivity of gross primary productivity to subsequent water stress increased significantly after initial drought and fire disturbances in the conterminous United States. Insect outbreak events, however, did not have significant impacts. Hot and dry environments generally exhibited increased sensitivity. Estimated ecosystem productivity and terrestrial carbon uptake decreased markedly with future warming scenarios due to the increased sensitivity to water stress. Our results highlight that intensifying disturbance regimes are likely to further impact forest sustainability and carbon sequestration, increasing potential risks to future terrestrial carbon sinks and climate change mitigation.

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Fig. 1: The sensitivity of GPP to water stress in CONUS changed notably after severe disturbances.
Fig. 2: The drought sensitivity of forests increased after severe disturbances.
Fig. 3: Drivers of the change in GPP drought sensitivity.
Fig. 4: Carbon uptake decreases in warming scenarios.

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Data availability

The NTSG Landsat GPP data were obtained from Google Earth Engine: https://developers.google.com/earth-engine/datasets/catalog. The GLASS GPP data were obtained from http://www.glass.umd.edu/Download.html. The EC-LUE GPP data were obtained from https://doi.org/10.6084/m9.figshare.8942336.v3. The NIRv GPP data were downloaded from https://doi.org/10.6084/m9.figshare.12981977.v2. The FLUXNET2015 GPP dataset is available at https://fluxnet.org/data/fluxnet2015-dataset/. The historical climatic data (for example, precipitation) and PDSI data were obtained from TerraClimate (https://www.climatologylab.org/terraclimate.html). The climatic data under +2 °C warming scenario were also obtained from TerraClimate (https://www.climatologylab.org/terraclimate.html). The MTBS maps of fire severity are available at https://www.mtbs.gov/direct-download. The land-cover maps were obtained from Earthdata (https://lpdaac.usgs.gov/products/mcd12q1v006/). The mean annual rates of mortality were from ref. 11, and no new mortality data were produced. The US boundary was from DATA.GOV (https://data.gov/). The data produced in this study are available via Figshare (https://doi.org/10.6084/m9.figshare.23730507)48.

Code availability

All analysis was done in the open-source software R. The code is available via Figshare (https://doi.org/10.6084/m9.figshare.23730507)48.

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Acknowledgements

The study was supported by the Wilkes Center at the University of Utah, and thanks to the Anderegg lab. J.P. was supported by the TED2021-132627B-I00 grant, funded by MCIN and the European Union NextGeneration EU/PRTR, and the CIVP20A6621 grant funded by the Fundación Ramón Areces. A.T.T. acknowledges funding from National Science Foundation grants 2003205, 2017949 and 2216855, the University of California Laboratory Fees Research Program award no. LFR-20-652467 and the Gordon and Betty Moore Foundation grant GBMF11974. W.R.L.A. acknowledges support from the David and Lucille Packard Foundation and US National Science Foundation grants 1802880, 2003017 and 2044937 as well as the Alan T. Waterman award IOS-2325700.

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Authors and Affiliations

  1. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    Meng Liu & William R. L. Anderegg

  2. Wilkes Center on Climate Science and Policy, University of Utah, Salt Lake City, UT, USA

    Meng Liu & William R. L. Anderegg

  3. Department of Geography, University of California, Santa Barbara, CA, USA

    Anna T. Trugman

  4. CREAF, Barcelona, Spain

    Josep Peñuelas

  5. Global Ecology Unit CREAF-CSIC-UAB, CSIC, Barcelona, Spain

    Josep Peñuelas

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Contributions

M.L. and W.R.L.A. conceptualized and designed the study with input from all co-authors. M.L. performed the analysis. M.L. wrote the initial draft and A.T.T., J.P. and W.R.L.A. discussed the design, analyses and results and provided extensive and valuable comments and revisions.

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Correspondence to Meng Liu.

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Extended data

Extended Data Fig. 1 The drought sensitivity increased significantly after severe droughts and fires when using SPEI to represent water stress.

(a–c) The change in sensitivity across CONUS after severe (a) droughts, (b) fires, and (c) insect outbreaks. The resolution of the distribution maps for fires and insect outbreaks was aggregated to 20 km for visual display. (d–f) The change in sensitivity among different land-cover types after severe (d) droughts (left to right, N=2668, 490, 5521, 1450, 8720), (e) fires (N=1944, 178, 258, 601, 6068), and (f) insect outbreaks (N=7320, 111, 548, 157, 3904). The error bars are standard errors. Asterisks indicate significance at the 0.05 level (two-sided) based on the GLS model. Multiple comparisons are not applicable. Definitions of disturbances for a pixel: droughts, SPEI < −1.2 (PDSI < ─3); fires, the proportion of burned area > 10%; insect outbreaks, insect caused mortality > 0.03%.

Extended Data Fig. 2 The best model for each pixel across CONUS.

a There are three models employed: linear (blue), quadratic (yellow), and logistic (red) models. The best model is defined as the one with the minimum Akaike Information Criterion (AIC). The linear model is the best for 69% of pixels across CONUS. b-c The (b) correlation between GPP anomaly and PDSI and (c) the corresponding significance (p < 0.05, two-sided t test). 60% of the available pixels present significant correlations between GPP anomaly and PDSI. d-f The change in sensitivity (Δk) for severe (d) droughts, (e) fires, and (f) insect outbreaks using the significant pixels in panel c. The results are comparable to those using all available pixels shown in Fig. 1.

Extended Data Fig. 3 The intercept of the GPP–PDSI model decreased significantly after severe disturbances.

(a–c) The changes in the intercept (Δb) across CONUS after severe (a) droughts, (b) fires, and (c) insect outbreaks. The resolution of the distribution maps for fires and insect outbreaks was aggregated to 20 km for visual display. Asterisks indicate significance at the 0.05 level (two-sided) based on the GLS model.

Extended Data Fig. 4 Land-cover map from the MCD12Q1 Type 5 classification in 2001.

ENF, evergreen needleleaf forest; EBF, evergreen broadleaf forest; and DBF, deciduous broadleaf forest.

Extended Data Fig. 5 Changes in drought sensitivity in undisturbed regions.

(a) The distribution of undisturbed regions in 1982–2018. (b) The trend of sensitivity (k) in undisturbed regions, where the trend was derived using an eight-year moving window, with k calculated for each window. The trend of sensitivity (Trend of k) is the slope of sensitivity vs year. (c) The mean trend of sensitivity for the land-cover types (left to right, N=258, 99, 2344, 228, 980), where the asterisks indicate significance (p = 0.002 and 0.003, respectively, two-sided) based on the GLS model. The error bars are standard errors. Multiple comparisons are not applicable. (d) The distribution of the trend of sensitivity (Trend of k) in climate space (mean annual temperature (MAT) vs mean annual precipitation (MAP)).

Extended Data Fig. 6 Correlation of the observed change in sensitivity and the Random Forest model estimated change in sensitivity.

(a–c) The scatterplots for severe (a) droughts, (b) fires, and (c) insect outbreaks. The red lines are the y = x lines, and orange color indicates high point density. The R2, slope, and p values (two-sided t test) are from linear regression: observed vs preidcted Δk. Multiple comparisons are not applicable.

Extended Data Fig. 7 Recovery time for the sensitivity to revert to its pre-disturbance level.

(a) A schematic to illustrate the definition of recovery time, where each circle means the sensitivity in an eight-year moving window, and the red dotted line indicates the identified recovery time (that is 5 years post-disturbance). (b–c) The distribution of recovery time derived from pixels with long post-distrubance time for severe (b) droughts and (c) fires. Pixels never recovered (gray color; ∼30% of pixels) are removed when calculating the mean recovery time. The resolution of the distribution map for fire was aggregated to 20 km for visual display.

Extended Data Fig. 8 Comparison of coefficients (sensitivity) of PDSI from different models.

Simple linear regression (SLR: GPPanomaly ~ PDSI) and multiple linear regression (MLR: GPPanomaly ~ Sradanomaly + Tanomaly + SManomaly + PDSI) are used based on data from 1982 to 2018. Each point in the figure indicates a pixel. The p value is from two-sided t test, and multiple comparisons are not applicable.

Extended Data Fig. 9 Comparisions of drought and fire return intervals and different thresholds as the minimum number of years for regression.

(a–b) The histograms of return intervals of severe (a) droughts and (b) fires in CONUS, where the bin width is one year. The red lines indicate return intervals of eight years. (c–d) The change in sensitivity when using (c) six years (left to right, N=3336, 591, 8500, 2012, 12744) and (d) ten years (N=1847, 228, 4167, 886, 4831) as the minimum for regression. The error bars are standard errors. Asterisks indicate significance at the 0.05 level (two-sided) when using the GLS model. Multiple comparisons are not applicable.

Extended Data Fig. 10 Changes in drought sensitivity using the four remotely sensed GPP products (NTSG, GLASS, EC-LUE and NIRv GPP) separately with PDSI representing water stress.

The asterisks indicate p < 0.05 (two-sided) based on the GLS model.

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Liu, M., Trugman, A.T., Peñuelas, J. et al. Climate-driven disturbances amplify forest drought sensitivity. Nat. Clim. Chang. 14, 746–752 (2024). https://doi.org/10.1038/s41558-024-02022-1

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