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Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests

Nature Climate Change volume 16, pages 187–192 (2026)Cite this article

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Abstract

Climate change is driving more frequent and severe wildfires in northwestern North American boreal forests, initiating shifts from conifer to broadleaf deciduous forest dominance. The resulting forests sequester more carbon and are more resistant to burning. However, when deciduous forests do burn, patterns and drivers of carbon losses are important for predicting long-term carbon storage in boreal forest landscapes. Here we use a combination of field and statistical modelling approaches to quantify carbon combustion losses in burned deciduous boreal forests. On average, deciduous forests lose less than half as much carbon to wildfire combustion as conifer forests per unit burned area. Although deciduous stands are more sensitive to top–down fire weather drivers than conifer stands, carbon loss is always lower than the minimum for conifer stands. This, along with the fire-suppressive effects of deciduous stands, could slow the positive feedback between wildfire and climate in fire-prone boreal landscapes.

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Fig. 1: Pre-fire carbon, post-fire carbon, carbon loss and proportional carbon loss in total, aboveground and belowground pools among stand types.
Fig. 2: Partial dependence of carbon loss on top–down and bottom–up drivers.
Fig. 3: Summary of important top–down and bottom–up drivers of carbon loss.

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Data availability

Data used in the final models are available from https://doi.org/10.6073/pasta/2c370c64d2d815b897846116f1a34efe (ref. 78). Raw data are available from https://doi.org/10.6073/pasta/72cb6cfbb9eea9ee6353b0f7c645510c (ref. 79), https://doi.org/10.6073/pasta/0aad7b4ce615a93ddebbb3e9c73386bb (ref. 80), https://doi.org/10.6073/pasta/5576635bb8051b84b7cada7e0bc5294f (ref. 81) and https://doi.org/10.6073/pasta/7f8ed51990e896cf8c4278533b8e1b5a (ref. 82).

Code availability

No custom code or mathematical algorithms were used in the analyses of these data. The R code for our statistical analyses is available at https://github.com/mack-walker-lab-nau/Black_FiSL.

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Acknowledgements

Funding was provided by the NSF Office of Polar Programs Arctic System Science (OPP-ARCSS) 2116864 awarded to M.C.M., X.J.W., S.J.G., L.T.B., B.M.R. and W.D.H.; NSF Arctic Natural Sciences (OPP-ANS) 2019485 awarded to X.J.W., M.C.M. and B.R.M.; NASA 80NSSC22K1244 awarded to X.J.W., M.C.M., L.T.B., S.J.G. and B.M.R.; and NASA 80NSSC22K1247 awarded to S.J.G., B.M.R., L.T.B. and M.C.M. Support was also provided by the NSF Long-Term Ecological Research (LTER) Program (grants nos. DEB-2224776, DEB-1636476, DEB-1026415, DEB-0620579, DEB-0423442, DEB-0080609, DEB-9810217, DEB-9211769 and DEB-8702629) and the USDA Forest Service Pacific Northwest Research Station (agreement RJVA-PNW-20-JV-11261932-018) awarded to M.C.M. We acknowledge the Yukon Scientists and Explorer Act for permission to conduct research in Yukon (22-66S&E). D. Auty provided valuable guidance on modelling approaches. C. Ebert, C. Truettner, S. Miller and M. Boyd contributed laboratory, field and analytical support. We are especially grateful to the many graduate and undergraduate students at Northern Arizona University who assisted in the field and laboratory. We thank C. Roland, S. Stehn and the National Park Service Central Alaska Network Vegetation Monitoring Program for providing Alaskan forest inventory data.

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Authors and Affiliations

  1. Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

    Betsy Black, Xanthe J. Walker & Michelle C. Mack

  2. USDA Forest Service, Pacific Southwest Research Station (ORISE), Redding, CA, USA

    Betsy Black

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

    Xanthe J. Walker, Logan T. Berner & Scott J. Goetz

  4. Woodwell Climate Research Center, Falmouth, MA, USA

    Jacqueline Dean, Stefano Potter, Brendan M. Rogers & Anna C. Talucci

  5. Cary Institute of Ecosystem Studies, Millbrook, NY, USA

    Winslow D. Hansen

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Contributions

B.B., X.J.W. and M.C.M. contributed to conceptualization, data curation, formal analysis and methodology. X.J.W., M.C.M., L.T.B., S.J.G., W.D.H. and B.M.R. contributed to funding acquisition. B.B., X.J.W. and M.C.M. were involved in investigations. X.J.W. and M.C.M. contributed to project administration and resources. B.B., S.P., J.D. and A.C.T. contributed to software. X.J.W. and M.C.M. contributed to supervision. All authors contributed to validation. B.B. contributed to visualization. B.B., X.J.W. and M.C.M. contributed to writing the original draft. All authors contributed to review and editing of the paper.

Corresponding author

Correspondence to Xanthe J. Walker.

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Extended data

Extended Data Table 1 Summary of landscape variables, pre-fire and post-fire stand structure, fire severity and combustion, and fire weather conditions by stand type
Full size table
Extended Data Table 2 Model-estimated means of pre-fire carbon (C), post-fire C, C loss, and proportional C loss across stand types
Full size table
Extended Data Table 3 Percent of total pre-fire carbon (C), post-fire C, C loss from belowground pools across stand types
Full size table
Extended Data Table 4 Candidate variables considered for analysis of drivers of total carbon (C) combustion
Full size table
Extended Data Table 5 Random forest model fit, predictive performance, and variable-removal diagnostics for total carbon loss
Full size table

Extended Data Fig. 1 Fire scars sampled in Alaska, USA and the Yukon, Canada.

Fire scars sampled in Alaska, USA1,2 and the Yukon, Canada3,4. All mixed, aspen, and birch plots were sampled in the summer of 2022. Conifer plots were sampled in 2005. The map highlights two regions (A and B), with the inset map showing the location of these regions within the EPA Level II Ecoregions of Alaska Boreal Interior and Boreal Cordillera 5,6. This map was created using ArcGIS Pro7 with World Terrain Reference and World Hillshade basemaps8.

Extended Data Fig. 2 Total wildfire carbon loss across fire scars.

Box plots show the distribution of carbon (C) loss in fire scars inventoried in the summers of 2005 (Boundary, Taylor Highway Complex, and Dall City) and 2022 (all others). Each box plot indicates the C loss median (center line), interquartile range (box), and values within 1.5 × the interquartile range (whiskers). Overlaid points represent individual survey plots, with point color denoting the dominant stand type. The number of plots sampled in each fire scar is shown above each box plot. The inset map shows the locations of sampled fire scars in Alaska1 and the Yukon Territory3. The inset map was created in RStudio9 using the terra package10.

Extended Data Fig. 3 Variable importance rankings of drivers of wildfire carbon loss in each stand type.

Each row contains variable importance ranking plots for random forest models created to predict drivers of carbon (C) loss in each stand type. Variable importance is ranked by variable contribution to node purity (decrease in Gini) in the left column and to permutation importance (decrease in mean squared error) in the right column. dCMI is spring Climate Moisture Index departure from normal. RH is day-of-burn relative humidity. Above(ground) and Below(ground) C are pre-fire C pool sizes. Stand Age is the age of trees in each site at the time of fire and Moisture is an index of soil moisture.

Extended Data Fig. 4 Comparison of partial dependence of carbon loss in aspen stands on predictor variables between two random forest models.

Top panel shows results from random forest fit using the full aspen dataset (Full model, n = 93) and bottom excludes sites with large ( ≥ 3 kg C m−2) pre-fire belowground C pools ( < 3000, n = 78). Numbers in the upper left corner of each plot indicate variable importance rank (node purity). Predictor variables include: dCMI, spring Climate Moisture Index departure from normal (mm, negative values indicate drier than normal conditions); RH, relative humidity (%) on DOB; Above(ground) and Below(ground) C, pre-fire C pool sizes (kg C m−2); Stand Age, tree age at the time of fire (years); and Moisture, an index of soil moisture (1=driest, 6=wettest). The smoothed partial dependence line represents the mean predicted C loss, and shaded ribbons show 95% confidence intervals obtained from 100 bootstrap resamples. In the subset model ( < 3000), pre-fire belowground C pool size remains the top predictor of C loss in aspen stands, but the directionality of the trend is much less strongly negative and the trend’s confidence intervals are less tightly constrained. This demonstrates that the plots with anomalously large pre-fire belowground C pools are primarily responsible for the negative relationship between C loss and pre-fire belowground C pool size.

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Black, B., Walker, X.J., Berner, L.T. et al. Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests. Nat. Clim. Chang. 16, 187–192 (2026). https://doi.org/10.1038/s41558-025-02539-z

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