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Asymmetric sensitivity of boreal forest resilience to forest gain and loss

Nature Ecology & Evolution volume 9, pages 505–514 (2025)Cite this article

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Abstract

Forest gains and losses may have unequal effects on forest resilience, particularly given their distinct temporal dynamics. Here, we quantify the sensitivities of boreal forest resilience to forest cover gain and loss using a resilience indicator derived from the temporal autocorrelation (TAC) of the kernel normalized difference vegetation index from 2000 to 2020. Our findings unveil pronounced asymmetric sensitivities, with stronger sensitivity to forest loss (−4.26 ± 0.14 × 10−3; TAC increase per 1% forest cover loss) than to forest gain (−1.65 ± 0.12 × 10−3; TAC decrease per 1% forest cover gain). Locally, ~73% of the boreal forest exhibits negative sensitivity, indicating enhanced resilience with forest cover gain and vice versa, especially in intact forests compared to managed ones. This sensitivity is affected by various trajectories in forest cover change, stemming primarily from temporal asynchrony in the recovery rates of various ecosystem functions. The observed asymmetry underscores the importance of prioritizing forest conservation over reactive management strategies following losses, ultimately contributing to more sustainable forest management practices.

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Fig. 1: Asymmetric sensitivity of ΔTAC to boreal forest gain and loss.
Fig. 2: Sensitivity of forest resilience to forest cover change across different forest types.
Fig. 3: Sensitivity of ΔTAC to ΔFC within pixels exhibiting distinct trajectories of forest loss.

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Data availability

The Köppen–Geiger world map is available at http://koeppen-geiger.vu-wien.ac.at/present.htm. NDVI data were sourced from the MOD13C1V6 product at https://lpdaac.usgs.gov/products/mod13c1v006/ (ref. 59). Forest cover fraction data were obtained from the MOD44BV6 product at https://lpdaac.usgs.gov/products/mod44bv006/ (ref. 60). VOD data were acquired from the VOD Climate Archive (VODCA) via Zenodo at https://zenodo.org/records/2575599 (ref. 71). Land-cover data were available from the ESA CCI at https://maps.elie.ucl.ac.be/CCI/viewer/download.php. The global forest management data are available via Zenodo at https://zenodo.org/records/5879022 (ref. 72). The PFT data were attained from the MCD12C1V6 product at https://lpdaac.usgs.gov/products/mcd12c1v006/ (ref. 61). GPP data were acquired from the MOD17A2HV6 product at https://lpdaac.usgs.gov/products/mod17a2hv006/ (ref. 62). The climate datasets are available from the ERA5-Land reanalysis product at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=download.

Code availability

The custom MATLAB (R2023a) scripts written to analyse the data and generate figures are available via Figshare at https://figshare.com/projects/Asymmetric_sensitivity_of_boreal_forest_resilience_to_forest_gain_and_loss/208609 (ref. 73).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China—United Nations Environment Programme (grant no. 42361144001 to Z.Z.), National Natural Science Foundation of China (42371026 to D.W. and 42071022 to Z.Z.), Shenzhen Science and Technology Project for Sustainable Development in Special Innovation (KCXFZ20230731093403008 to Z.Z., D.W. and X.L.) and the start-up and high-level special funds provided by the Southern University of Science and Technology (29/Y01296602, 29/Y01296122, 29/Y01296222 and G030290001 to Z.Z.). A.C. acknowledges support from an Oak Ridge National Lab subcontract (CW53561). We thank the Center for Computational Science and Engineering at the Southern University of Science and Technology for providing computing resources.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Xiaoye Liu, Dashan Wang & Zhenzhong Zeng

  2. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

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Contributions

Z.Z. and D.W. designed the research. X.L. performed the analysis and wrote the first draft. All authors contributed to the interpretation of the results and the revising of the paper.

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Correspondence to Dashan Wang or Zhenzhong Zeng.

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Extended data

Extended Data Fig. 1 Spatial distribution of target and control pixels over boreal regions.

a, Spatial map of the distribution of target pixels and control pixels exhibiting no discernible forest cover (FC) change. b, A zoomed-in view of a 1° × 1° spatial window used for extracting ΔTAC, with the target pixel (green) located at the center with reference pixels (red; both at 0.05° × 0.05° resolution) scattering around it. c, Probability distribution frequency of the number of control pixels within the corresponding 1° × 1° spatial windows.

Extended Data Fig. 2 Asymmetric impacts of boreal forest cover change on resilience derived from alternative datasets and methods.

Each row corresponds to one alternative data or method: VOD as the state variable for the forest ecosystem to construct TAC (a, b), ESA CCI land cover product as an alternative dataset to derive forest cover fraction (c, d), variance as an alternative resilience indicator (e, f), 3-order harmonic deseasoner with rolling mean detrender as complementary pre-processing approaches (g, h). Bars in panels a, c, e and g indicate the mean ΔTAC weighted by pixel areas, with error bars representing the 95% confidence interval of the mean ΔTAC. ΔFC values in panel a were divided into 40 bins with 0.5% intervals, while in panels c, e and g, they were divided into 25 bins with 2% intervals. Bars in panels b, d, f and h indicate the linear regression coefficient, with error bars representing the 95% confidence interval. Brown and magenta numbers in panels indicate the sample size of forest loss pixels, while green numbers indicate the sample size of forest gain pixels.

Extended Data Fig. 3 Comparative analysis of forest resilience and its sensitivity to forest cover change under different plant function types (PFTs).

a, Distribution of resilience (long-term TAC) among PFTs. Box plots illustrate the median TAC with its 25th and 75th percentiles. The double asterisks (**) indicate statistically significant differences in the medians between each pair of types (two-sided Mann-Whitney U test; p < 0.01). b, Regional sensitivity of resilience to forest cover change among PFTs. The regional sensitivity is determined by regressing ΔTAC against ∆FC across the entire boreal region. Bars denote the regression coefficient, with error bars representing the 95% confidence interval for the coefficient. The double asterisks (**) indicate statistically significant differences in the regression coefficients between each pair of types (two-sided Wald test; p < 0.01). c, Distribution of local sensitivity, determined by regressing ΔTAC against ∆FC in a 1° × 1° spatial window surrounding the target pixel. Box plots illustrate the median TAC with its 25th and 75th percentiles. The double asterisks (**) indicate statistically significant differences in the medians between each pair of types (two-sided Mann-Whitney U test; p < 0.01). Numbers in panels a-c represent the sample size (pixels) of each forest type.

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Liu, X., Wang, D., Chen, A. et al. Asymmetric sensitivity of boreal forest resilience to forest gain and loss. Nat Ecol Evol 9, 505–514 (2025). https://doi.org/10.1038/s41559-024-02631-1

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