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Regional fire–greening positive feedback loops in Alaskan Arctic tundra

Nature Plants volume 10, pages 1886–1891 (2024)Cite this article

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Abstract

Arctic tundra has experienced rapid warming, outpacing global averages, leading to significant greening whose primary drivers include widespread shrubification. Here we confirm that a fire–greening positive feedback loop is evident across the Alaskan tundra, and evidence suggests that this feedback loop is dominated by the fire–shrub interactions. We show that tundra wildfires, especially those with higher severity, play a critical role in boosting the overall greening of the tundra, often by enhancing upright deciduous shrub growth or establishment but sometimes by inducing increases in other vascular biomass. In addition, fire–greening interactions vary greatly within different tundra subregions, a likely consequence of the spatial heterogeneity in vegetation composition, climatic and geophysical conditions.

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Fig. 1: The post-fire NDVImax anomaly trajectories generated using Landsat data.
Fig. 2: Boxplots generated based on field data.

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Data availability

All data used in this paper are publicly accessible. The field data that our team collected are available through the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) at https://doi.org/10.3334/ORNLDAAC/1919. The AKVEG is available at https://akveg.uaa.alaska.edu/. The MTBS product is available at https://www.mtbs.gov/. The CAVM dataset is available at https://www.geobotany.uaf.edu/cavm/. The LandsatTS package for R is available via GitHub at https://github.com/logan-berner/LandsatTS. Visualizations in this paper were implemented using ArcGIS Desktop (v10.6), and R (v3.5.1). Data analyses were carried out using R (v3.5.1), Python (v2.7.14), IDL (v8.5) and GEE.

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Acknowledgements

This study was funded by NASA ABoVE and was supported by the NASA Terrestrial Ecology programme grant NNX15AT79A (T.V.L.).

Author information

Authors and Affiliations

  1. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Dong Chen, Zhihao Wang, Allison Bredder & Tatiana V. Loboda

  2. College of Information and Electrical Engineering, China Agricultural University, Beijing, China

    Cheng Fu

  3. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, China

    Cheng Fu

  4. Department of Geography, University of Zurich, Zurich, Switzerland

    Cheng Fu

  5. Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, USA

    Liza K. Jenkins

  6. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jiaying He

  7. Alaska Fire Science Consortium, University of Alaska, Fairbanks, AK, USA

    Randi R. Jandt

  8. Alaska Biological Research, Inc., Fairbanks, AK, USA

    Gerald V. Frost

  9. School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    Logan T. Berner

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Contributions

D.C.: conceptualization, methodology, software, data curation, field work, writing – original draft, writing – review and editing, visualization and project administration. C.F.: methodology and software. L.K.J.: field work. J.H.: field work and writing – review and editing. Z.W.: methodology and software. R.R.J.: writing – review and editing. G.V.F.: writing – review and editing. A.B.: writing – original draft, and writing – review and editing. L.T.B.: writing – review and editing. T.V.L.: field work, writing – review and editing, project administration and funding acquisition.

Corresponding author

Correspondence to Dong Chen.

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Extended data

Extended Data Fig. 1 Distribution of field sites and the four tundra subregions.

The boundary of the Arctic tundra is delineated based on the Circumpolar Arctic Vegetation Map34 (CAVM). Historical fire perimeters (red) are based on the Alaska Large Fire Database45 from 1940 to 2021. Field data in Seward and Noatak were collected by our team, which are available at https://doi.org/10.3334/ORNLDAAC/1919.

Extended Data Fig. 2 Distribution of random sample points generated across the Alaskan tundra.

Red, green, and blue points represent burned, unburned, and background sites, respectively. Panel (a) shows an overall view across the Alaskan tundra, while panels (b) and (c) offer close-up views of two specific locations.

Extended Data Fig. 3 The distribution of AKVEG field points.

The distribution of the tundra field points that the Alaska Vegetation Plots Database (AKVEG)32 contains.

Extended Data Fig. 4 Scatterplots of field-measured shrub and graminoid cover and NDVImax by years since fire based on Landsat imagery on GEE.

a–l, Panels correspond to tundra subregions North Slope (a–c), Noatak (d–f), Seward (g–i), and Southwest (j–l). m–o, Panels correspond to all of the Alaskan tundra. Symbols ‘*’ and ‘**’ after the correlation (R) values correspond to p < 0.01 and 0.001, respectively. Field data were from AKVEG. DS: deciduous shrub; ES: evergreen shrub; GD: graminoid.

Extended Data Fig. 5 Mean biomass of four dominant shrub species for each stand age group calculated based on 1 x 1m plots in Noatak (a) and Seward (b).

YSF: year since fire. Birch: Betula nana; Blueberry: Vaccinium uliginosum; Ledum: Rhododendron groenlandicum (bog Labrador tea); Salix: Salix spp. Error bars denote ±1 standard error. Crosses represent the values of the mean biomass. The actual data, including the numbers of observations, are listed in Supplementary Table 2.

Extended Data Fig. 6 Post-fire NDVImax anomaly trajectories for different physiognomic types and for high severity level burned points.

Post-fire NDVImax anomaly trajectories for different physiognomic types (according to the 1 km CAVM raster dataset44) and for high severity level burned points. a–d, Tundra subregions North Slope (a), Noatak (b), Seward (c) and Southwest (d). Error bars denote ±1 standard error. The pie charts on the right show the relative proportions of the different physiognomic types among all high severity burned points in the corresponding subregion. The actual data, including the numbers of observations, are listed in Supplementary Table 3 (the trajectories) and Supplementary Table 4 (the pie charts), respectively.

Extended Data Fig. 7 Proportions of mean total biomass of four dominant shrub species for each stand age group calculated based on 1 x 1m plots in Noatak (a) and Seward (b).

YSF: year since fire. Birch: Betula nana; Blueberry: Vaccinium uliginosum; Ledum: Rhododendron groenlandicum (bog Labrador tea); Willow: Salix spp. Error bars denote ±1 standard error. Crosses represent the values of the mean biomass. The actual data, including the numbers of observations, are listed in Supplementary Table 5.

Extended Data Fig. 8 NDVImax trajectories between 2001 and 2021 based on matching burned and background sample points.

a–d, Panels correspond to tundra subregions North Slope (a), Noatak (b), Seward (c) and Southwest (d). Error bars denote ±1 standard error. The actual data, including the numbers of observations, are listed in Supplementary Table 6.

Supplementary information

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Chen, D., Fu, C., Jenkins, L.K. et al. Regional fire–greening positive feedback loops in Alaskan Arctic tundra. Nat. Plants 10, 1886–1891 (2024). https://doi.org/10.1038/s41477-024-01850-5

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