Abstract
Savannas cover a fifth of the land surface and contribute a third of terrestrial net primary production, accounting for three-quarters of global area burned and more than half of global fire-driven carbon emissions1,2,3. Fire suppression and afforestation have been proposed as tools to increase carbon sequestration in these ecosystems2,4. A robust quantification of whole-ecosystem carbon storage in savannas is lacking however, especially under altered fire regimes. Here we provide one of the first direct estimates of whole-ecosystem carbon response to more than 60 years of fire exclusion in a mesic African savanna. We found that fire suppression increased whole-ecosystem carbon storage by only 35.4 ± 12% (mean ± standard error), even though tree cover increased by 78.9 ± 29.3%, corresponding to total gains of 23.0 ± 6.1 Mg C ha−1 at an average of about 0.35 ± 0.09 Mg C ha−1 year−1, more than an order of magnitude lower than previously assumed4. Frequently burned savannas had substantial belowground carbon, especially in biomass and deep soils. These belowground reservoirs are not fully considered in afforestation or fire-suppression schemes but may mean that the decadal sequestration potential of savannas is negligible, especially weighed against concomitant losses of biodiversity and function.
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Data and code availability
Data and code are available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.pg4f4qrr5. Source data are provided with this paper.
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Acknowledgements
We gratefully acknowledge the logistical support provided by South African National Parks staff in Kruger National Park. Y.Z. was supported by a G. Evelyn Hutchinson Environmental Postdoctoral Fellowship from the Yale Institute for Biospheric Studies, A.C.S. was partially supported by a grant from the United States National Science Foundation (NSF MSB-1802453) and by funding from Yale University, J.S., P.B.B., E.G.H. and A.B.D. from Harvard University, and J.R.B. from the USDA Forest Service, Southern Research Station.
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Conceptualization: Y.Z. and A.C.S.; methodology: Y.Z., J.S., J.R.B., P.B.B., A.B.D. and A.C.S.; investigation: Y.Z., C.C., M.F.C., E.G.H., A.B.D. and A.C.S.; visualization: Y.Z. and A.C.S.; funding acquisition: A.B.D. and A.C.S.; project administration: A.B.D. and A.C.S.; supervision: A.B.D. and A.C.S.; writing — original draft: Y.Z. and A.C.S.; writing — review and editing: Y.Z., J.S., J.R.B., C.C., P.B.B., M.F.C., E.G.H., A.B.D. and A.C.S.
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Extended data figures and tables
Extended Data Fig. 1 An example showing belowground to aboveground biomass allocation for resprouting Terminalia sericea.
a, b, Five T. sericea individuals that have experienced annual burning were excavated in the Pretoriuskop landscape in Kruger National Park, South Africa. c, The difference between aboveground and belowground biomass and the ratio of belowground to aboveground biomass was 19.5. The box plots show medians (that is, 50th percentile), 25th and 75th percentiles, and the approximate 95% confidence interval for five replicates.
Extended Data Fig. 2 Maps showing the study site.
Maps showing the locations of different fire treatments (that is, annual, triennial and unburned) examined in this study and located in each string (Fayi, Kambeni, Numbi and Shabeni) across the Pretoriuskop landscape at Kruger National Park, South Africa. Base map for South Africa modified from Natural Earth.
Extended Data Fig. 3 Changes in SOC storage and soil δ13C across different fire treatments throughout the 60-cm soil column.
Effects of fire treatments on total SOC storage (Mg C ha−1) (a), soil δ13C (‰) (b), C3-derived SOC storage (that is, from woody plants) (Mg C ha−1) (c) and C4-derived SOC storage (that is, from grasses) (Mg C ha−1) (d). Values are mean ± standard errors (n = 4).
Extended Data Fig. 4 Long-term monitoring of grass fuel loads and their correlation to LiDAR-derived mean grass height.
a–c, Grass fuel loads (kg ha−1) for annual (a), triennial (b) and April B2 (that is, burning in April for every two years, as a proxy for unburned) (c) treatments from 1982 to 2009 for different strings at the Pretoriuskop landscape in Kruger National Park, South Africa. Disconnected lines indicate missing data for specific years. d, The correlation between averaged grass fuel loads from 1982 to 2009 and LiDAR-derived mean grass heights (m) (R2 = 0.38, P = 0.03). The mean grass height was calculated by averaging heights of pixels that range from 0.05 to 0.5 m in the CHM derived from LiDAR. Please note especially that, in panel d, LiDAR-derived mean grass height was estimated from the unburned treatment itself, but that field-estimated grass fuel load was estimated from the April B2 treatment as a proxy (as grass fuel load is not routinely measured for the unburned treatment).
Extended Data Fig. 5 The uncertainty of coarse lateral and taproot biomass estimates.
a, The uncertainty of coarse lateral and taproot biomass for each treatment replicate. Error bars indicate the 95% confidence interval for coarse lateral and taproot biomass estimates derived from fitting regression lines (see Supplementary Figs. 5 and 10). Coarse-lateral-root biomass estimates were significantly correlated with taproot biomass estimates (R2 = 0.75, P < 0.001). Letters F, K, N and S indicate Fayi, Kambeni, Numbi and Shabeni strings at the Pretoriuskop landscape in Kruger National Park, South Africa; letters A, T and U indicate annual, triennial and unburned treatments. b, c The uncertainty of (that is, lower bound, mean and upper bound) coarse lateral and taproot biomass across different fire treatments. The box plots show medians (that is, 50th percentile), 25th and 75th percentiles, and the 95% confidence interval for four replicates. Points in b and c indicate outliers.
Extended Data Fig. 6 Depth distribution of coarse-lateral-root biomass across fire treatments and soil sand content.
a, Depth distribution of the GPR index (% in the number of pixels above the threshold for root detections) as an indicator of coarse-lateral-root biomass allocation throughout the soil column across different fire treatments at each string. Horizontal lines indicate the depth (cm) at which the GPR index reaches 50% of the total detections in the 60-cm soil column. b, Effects of fire treatment on the depth distribution of coarse-lateral-root biomass (P = 0.51). The box plots show medians (that is, 50th percentile), 25th and 75th percentiles, and the 95% confidence interval for four replicates. c, The correlation between soil sand content (%) and depth distribution of coarse-lateral-root biomass (R2 = 0.61, P = 0.003). The regression line indicates the significant linear fit and the shaded bands illustrate the 95% confidence interval of the linear fit.
Extended Data Fig. 7 The correlation between ratio of belowground to aboveground carbon storage and tree cover (%) (R2 = 0.83, P < 0.0001).
The regression line indicates the significant linear fit and the shaded bands illustrate the 95% confidence interval of the linear fit.
Extended Data Fig. 8 The validation of the object-based method to estimate aboveground woody biomass.
a, The correlation between LiDAR-derived stem density for trees with height > 5m (trees ha−1) and field-measured stem density (trees ha−1). The field-measured stem density was from ref. 52, which surveyed tree heights in eight 10-m-radius plots at each annual, triennial and unburned treatment in Kambeni, Numbi and Shabeni strings at the Pretoriuskop landscape in Kruger National Park, South Africa. The regression line indicates the significant linear fit and the shaded bands illustrate the 95% confidence interval of the linear fit. The dashed line indicates the 1:1 line. b, Differences in aboveground woody biomass between allometric-derived, object-based and plot-averaged estimates. The allometric-derived biomass estimation was on the basis of species-specific allometric equations developed in ref. 54, which predict aboveground woody biomass from DBH. This estimation was calculated for trees with DBH > 5 cm in each 10 × 10-m plot. The plot-averaged LiDAR biomass was estimated using an allometric equation derived from on-the-ground plot-level sampling relating aboveground woody biomass to LiDAR-derived canopy height and canopy area (aboveground woody biomass = −11.5 + 25.8 * canopy height * canopy area); please refer to ref. 21 for more details. The canopy height and canopy area were averaged across pixels with height > 0.5 m in each 30-m-radius plot. The box plots show medians (that is, 50th percentile), 25th and 75th percentiles, and the 95% confidence interval for four replicates. Points in b indicate outliers.
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Zhou, Y., Singh, J., Butnor, J.R. et al. Limited increases in savanna carbon stocks over decades of fire suppression. Nature 603, 445–449 (2022). https://doi.org/10.1038/s41586-022-04438-1
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DOI: https://doi.org/10.1038/s41586-022-04438-1
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