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Pervasive but biome-dependent relationship between fragmentation and resilience in forests

Nature Ecology & Evolution volume 9, pages 1670–1684 (2025)Cite this article

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Abstract

The relationship between landscape fragmentation and vegetation resilience is uncertain. Here we use multiple satellite-based tree cover data and vegetation indices to quantify the apparent effects of fragmentation on global forest resilience and potential mechanisms thereof. We measure fragmentation as edge density, patch density and mean patch area of tree cover patches, and measure resilience as one-lag temporal autocorrelation of vegetation indices. We find a statistically significant (P < 0.05) fragmentation–resilience relationship in about 77% of fragmented forests, but the direction varies across biomes. In tropical and temperate forests, fragmentation is linked to increased local temperature and atmospheric dryness, resulting in a negative fragmentation–resilience relationship. Conversely, in boreal forests, fragmentation is associated with decreased atmospheric dryness and enhanced light resource, thereby increasing forest resilience. Our results reconcile competing hypotheses and highlight the importance of accounting for fragmentation when predicting shifts in ecosystem resilience under disturbances. These findings also suggest the necessity of biome-targeted forest management strategies for climate change mitigation and adaptation.

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Fig. 1: Pervasive but divergent fragmentation–resilience relationships across global forests estimated using the PCA method.
Fig. 2: Comparisons of fragmentation versus tree cover impacts on forest resilience using the PLS-SEM method.
Fig. 3: Sensitivity (βTAC) of TACkNDVI to ED for each TC bin estimated using the statistical data binning approach.
Fig. 4: Influences of fragmentation on the biophysical effects of forest on local climate.
Fig. 5: Influencing pathways of fragmentation on forest resilience.
Fig. 6: Fragmentation–resilience relationships in different conditions.

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Data availability

The global tree cover, land cover, NDVI, EVI, GOSIF, LAI, LST, VPD, SM and APAR data used for the analyses in this study are available online as follows: GFW tree cover images https://glad.earthengine.app/view/global-forest-change; GFCC tree cover images https://lpdaac.usgs.gov/products/gfcc30tcv003/; MODIS MOD13C2 NDVI, EVI datasets https://lpdaac.usgs.gov/products/mod13c2v061/; MODIS MCD12C1 land cover images https://lpdaac.usgs.gov/products/mcd12c1v061/; MODIS MOD11C3 LST datasets https://lpdaac.usgs.gov/products/mod11c3v061/; MODIS MCD15A3H LAI datasets https://lpdaac.usgs.gov/products/mcd15a3hv061/; TerraClimate VPD and SM datasets https://www.climatologylab.org/terraclimate.html; GLASS PAR and FPAR datasets http://www.glass.umd.edu/Download.html; GOSIF dataset https://www.mdpi.com/2072-4292/11/5/517/s1; ESA WorldCover 2021 10-m land cover datasets https://doi.org/10.5281/zenodo.7254221 (ref. 133); MODIS MCD43A3 black-sky albedo and white-sky albedo datasets for bands 1 to 7, as well as for the visible, near-infrared and shortwave bands https://lpdaac.usgs.gov/products/mcd43a3v061/; global aridity index and potential evapotranspiration database https://doi.org/10.6084/m9.figshare.7504448.v5 (ref. 134); the key code for mapping the inundation extent and duration of global flood events https://doi.org/10.5281/zenodo.11181120 (ref. 135); the digital Köppen–Geiger world map http://koeppen-geiger.vu-wien.ac.at/present.htm; drivers of global forest loss https://www.science.org/doi/abs/10.1126/science.aau3445; global datasets of forest aboveground biomass https://catalogue.ceda.ac.uk/uuid/af60720c1e404a9e9d2c145d2b2ead4e; global long-term microwave vegetation optical depth climate archive (VODCA) https://zenodo.org/records/2575599 (ref. 136); and the FLUXNET2025 dataset https://fluxnet.org/data/fluxnet2015-dataset/.

Code availability

The code used for this study is available via Zenodo at https://zenodo.org/records/15488956 (ref. 137).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos. 42225104, 42471326 and 42125105), the National Key R&D Programme of China (no. 2024YFF1306600) and the Science and Technology Program of Guangdong (no. 2024B1212070012). C.W. was funded by the National Natural Science Foundation of China (42125101). J.C. was funded by the research grants ECO-FUN (PID2023-151488OB-I00) and MICROCLIM (PID2020-117636GB-C21).

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Authors and Affiliations

  1. State Key Laboratory for Ecological Security of Regions and Cities, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Yongxian Su, Chaoqun Zhang & Weiqi Zhou

  2. Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China

    Chaoqun Zhang, Xueqin Yang, Jiashun Ren & Xiuzhi Chen

  3. European Commission, Joint Research Centre, Ispra, Italy

    Alessandro Cescatti

  4. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Kailiang Yu & Chaoyang Wu

  5. Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif sur Yvette, France

    Philippe Ciais & Liyang Liu

  6. Institute of Geosciences, Universität Potsdam, Potsdam, Germany

    Taylor Smith

  7. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

    Jiali Shang

  8. Department of Evolutionary Biology, Environmental Sciences and Ecology, University of Barcelona/CREAF/IRBIO, Barcelona, Spain

    Jofre Carnicer

  9. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    Jane Liu & Jing Ming Chen

  10. Department of Environmental Science, University of Arizona, Tucson, AZ, USA

    Julia K. Green & Guillermo E. Ponce-Campos

  11. Guangzhou Institute of Geography, Guangzhou, China

    Jianping Wu

  12. International Institute for Earth System Sciences, Nanjing University, Nanjing, China

    Yongguang Zhang

  13. Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai, China

    Zhiyan Zuo

  14. School of Ecology and Environmental Science, Yunnan University, Kunming, China

    Jinbao Liao & Jianping Wu

  15. Department of Soil, Plant and Food Sciences, University of Bari ‘Aldo Moro’, Bari, Italy

    Raffaele Lafortezza

  16. Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, Beijing, China

    Raffaele Lafortezza

  17. Center for GeoData and Analysis, State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Kai Yan

  18. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Wenping Yuan

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  1. Yongxian Su
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  2. Chaoqun Zhang
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Contributions

Y.S. and C.Z. designed the study and wrote the initial paper. C.W. and W.Z. designed the study and revised the paper. C.Z. collected the data and performed the analysis. All authors contributed to discuss the scientific question and revise the paper.

Corresponding authors

Correspondence to Yongxian Su, Chaoqun Zhang, Chaoyang Wu or Weiqi Zhou.

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Nature Ecology & Evolution thanks Celso Silva-Junior and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–39 and Tables 1 and 2.

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Su, Y., Zhang, C., Cescatti, A. et al. Pervasive but biome-dependent relationship between fragmentation and resilience in forests. Nat Ecol Evol 9, 1670–1684 (2025). https://doi.org/10.1038/s41559-025-02776-7

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