in

Disturbance suppresses the aboveground carbon sink in North American boreal forests

  • 1.

    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    CAS 
    Article 

    Google Scholar 

  • 2.

    Lindroth, A., Grelle, A. & Morén, A.-S. Long-term measurements of boreal forest carbon balance reveal large temperature sensitivity. Glob. Change Biol. 4, 443–450 (1998).

    Article 

    Google Scholar 

  • 3.

    Kasischke, E. S., Christensen, N. Jr & Stocks, B. J. Fire, global warming, and the carbon balance of boreal forests. Ecol. Appl. 5, 437–451 (1995).

    Article 

    Google Scholar 

  • 4.

    Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).

    CAS 
    Article 

    Google Scholar 

  • 5.

    Welp, L. R. et al. Increasing summer net CO2 uptake in high northern ecosystems inferred from atmospheric inversions and comparisons to remote-sensing NDVI. Atmos. Chem. Phys. 16, 9047–9066 (2016).

    CAS 
    Article 

    Google Scholar 

  • 6.

    Myneni, R. B., Keeling, C., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    CAS 
    Article 

    Google Scholar 

  • 7.

    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    CAS 
    Article 

    Google Scholar 

  • 8.

    Girardin, M. P. et al. No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO2 fertilization. Proc. Natl Acad. Sci. USA 113, E8406–E8414 (2016).

    CAS 
    Article 

    Google Scholar 

  • 9.

    Giguère-Croteau, C. et al. North America’s oldest boreal trees are more efficient water users due to increased [CO2], but do not grow faster. Proc. Natl Acad. Sci. USA 116, 2749–2754 (2019).

    Article 
    CAS 

    Google Scholar 

  • 10.

    Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).

    CAS 
    Article 

    Google Scholar 

  • 11.

    Kasischke, E. S. & Turetsky, M. R. Recent changes in the fire regime across the North American boreal region—spatial and temporal patterns of burning across Canada and Alaska. Geophys. Res. Lett. 33, L09703 (2006).

    Google Scholar 

  • 12.

    White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C. & Hobart, G. W. A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ. 194, 303–321 (2017).

    Article 

    Google Scholar 

  • 13.

    Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).

    CAS 
    Article 

    Google Scholar 

  • 14.

    Ma, Z. et al. Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc. Natl Acad. Sci. USA 109, 2423–2427 (2012).

    CAS 
    Article 

    Google Scholar 

  • 15.

    Wang, J. A. et al. Extensive land cover change across Arctic–boreal northwestern North America from disturbance and climate forcing. Glob. Change Biol. 26, 807–822 (2020).

    Article 

    Google Scholar 

  • 16.

    Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S. & Mack, M. C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Change Biol. 16, 1281–1295 (2010).

    Article 

    Google Scholar 

  • 17.

    Wang, J. A. & Friedl, M. A. The role of land cover change in Arctic–boreal greening and browning trends. Environ. Res. Lett. 14, 125007 (2019).

    Article 

    Google Scholar 

  • 18.

    Beck, P. S. & Goetz, S. J. Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences. Environ. Res. Lett. 6, 045501 (2011).

    Article 

    Google Scholar 

  • 19.

    de Groot, W. J., Flannigan, M. D. & Cantin, A. S. Climate change impacts on future boreal fire regimes. For. Ecol. Manage. 294, 35–44 (2013).

    Article 

    Google Scholar 

  • 20.

    Bond-Lamberty, B., Peckham, S. D., Ahl, D. E. & Gower, S. T. Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature 450, 89–92 (2007).

    CAS 
    Article 

    Google Scholar 

  • 21.

    Bradshaw, C. J. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).

    Article 

    Google Scholar 

  • 22.

    Goulden, M. L. et al. Patterns of NPP, GPP, respiration, and NEP during boreal forest succession. Glob. Change Biol. 17, 855–871 (2011).

    Article 

    Google Scholar 

  • 23.

    Pugh, T. A. M., Arneth, A., Kautz, M., Poulter, B. & Smith, B. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat. Geosci. 12, 730–735 (2019).

    CAS 
    Article 

    Google Scholar 

  • 24.

    Zimov, S. et al. Contribution of disturbance to increasing seasonal amplitude of atmospheric CO2. Science 284, 1973–1976 (1999).

    CAS 
    Article 

    Google Scholar 

  • 25.

    Sedano, F. & Randerson, J. T. Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences 11, 3739–3755 (2014).

    Article 

    Google Scholar 

  • 26.

    Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520–523 (2019).

    CAS 
    Article 

    Google Scholar 

  • 27.

    Matasci, G. et al. Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots. Remote Sens. Environ. 216, 697–714 (2018).

    Article 

    Google Scholar 

  • 28.

    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

    CAS 
    Article 

    Google Scholar 

  • 29.

    Wulder, M. A., Hermosilla, T., White, J. C. & Coops, N. C. Biomass status and dynamics over Canada’s forests: disentangling disturbed area from associated aboveground biomass consequences. Environ. Res. Lett. 15, 094093 (2020).

    CAS 
    Article 

    Google Scholar 

  • 30.

    Margolis, H. A. et al. Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America. Can. J. For. Res. 45, 838–855 (2015).

    Article 

    Google Scholar 

  • 31.

    Neigh, C. S. et al. Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR. Remote Sens. Environ. 137, 274–287 (2013).

    Article 

    Google Scholar 

  • 32.

    Fisher, J. B. et al. Missing pieces to modeling the Arctic–boreal puzzle. Environ. Res. Lett. 13, 020202 (2018).

    Article 

    Google Scholar 

  • 33.

    Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).

    CAS 
    Article 

    Google Scholar 

  • 34.

    Kurz, W. A. et al. Carbon in Canada’s boreal forest—a synthesis. Environ. Rev. 21, 260–292 (2013).

    CAS 
    Article 

    Google Scholar 

  • 35.

    Price, D., Peng, C., Apps, M. & Halliwell, D. Simulating effects of climate change on boreal ecosystem carbon pools in central Canada. J. Biogeogr. 26, 1237–1248 (1999).

    Article 

    Google Scholar 

  • 36.

    Stocks, B. et al. Large forest fires in Canada, 1959–1997. J. Geophys. Res. Atmos. 107, FFR-5 (2002).

    Google Scholar 

  • 37.

    Kasischke, E. S., Williams, D. & Barry, D. Analysis of the patterns of large fires in the boreal forest region of Alaska. Int. J. Wildland Fire 11, 131–144 (2002).

    Article 

    Google Scholar 

  • 38.

    Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article 

    Google Scholar 

  • 39.

    Fredeen, A. L., Waughtal, J. D. & Pypker, T. G. When do replanted sub-boreal clearcuts become net sinks for CO2? For. Ecol. Manage. 239, 210–216 (2007).

    Article 

    Google Scholar 

  • 40.

    Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 201407302 (2014).

    Google Scholar 

  • 41.

    Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Article 
    CAS 

    Google Scholar 

  • 42.

    Natali, S. M. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Change 9, 852–857 (2019).

    CAS 
    Article 

    Google Scholar 

  • 43.

    Gedalof, Z. & Berg, A. A. Tree ring evidence for limited direct CO2 fertilization of forests over the 20th century: limited CO2 fertilization of forests. Glob. Biogeochem. Cycles 24, GB3027 (2010).

    Article 
    CAS 

    Google Scholar 

  • 44.

    Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    CAS 
    Article 

    Google Scholar 

  • 45.

    Duncanson, L. et al. Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California. Remote Sens. Environ. 242, 111779 (2020).

    Article 

    Google Scholar 

  • 46.

    Helbig, M., Pappas, C. & Sonnentag, O. Permafrost thaw and wildfire: equally important drivers of boreal tree cover changes in the Taiga Plains, Canada. Geophys. Res. Lett. 43, 1598–1606 (2016).

    Article 

    Google Scholar 

  • 47.

    Carpino, O. A., Berg, A. A., Quinton, W. L. & Adams, J. R. Climate change and permafrost thaw-induced boreal forest loss in northwestern Canada. Environ. Res. Lett. 13, 084018 (2018).

    Article 

    Google Scholar 

  • 48.

    Margolis, H., Sun, G., Montesano, P. M. & Nelson, R. F. NACP LiDAR-Based Biomass Estimates, Boreal Forest Biome, North America, 2005–2006 (ORNL DAAC, 2015); https://doi.org/10.3334/ORNLDAAC/1273

  • 49.

    Spawn, S. A. & Gibbs, H. K. Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010 (ORNL DAAC, 2020); https://doi.org/10.3334/ORNLDAAC/1763

  • 50.

    Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-ground Biomass for the Year 2017 Version 1 (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084

  • 51.

    Omernik, J. M. & Griffith, G. E. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ. Manage. 54, 1249–1266 (2014).

    Article 

    Google Scholar 

  • 52.

    Wulder, M. A. et al. Monitoring Canada’s forests. Part 1: completion of the EOSD land cover project. Can. J. Remote Sens. 34, 549–562 (2008).

    Article 

    Google Scholar 

  • 53.

    Jin, S., Yang, L., Zhu, Z. & Homer, C. A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sens. Environ. 195, 44–55 (2017).

    Article 

    Google Scholar 

  • 54.

    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS 
    Article 

    Google Scholar 

  • 55.

    Roy, D. P., Boschetti, L., Justice, C. & Ju, J. The collection 5 MODIS burned area product—global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690–3707 (2008).

    Article 

    Google Scholar 

  • 56.

    Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article 

    Google Scholar 

  • 57.

    R Core Team R: A Language and Environment for Statistical Computing Version 3.6.0 (R Foundation for Statistical Computing, 2019).

  • 58.

    Greenwell, B., Boehmke, B., Cunningham, J. & GMB Developers. gbm: Generalized Boosted Regression Models Version 2.1.5. R package (2019).

  • 59.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 

    Google Scholar 

  • 60.

    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315 (2017).

    Article 

    Google Scholar 

  • 61.

    Wang, J. A., Sulla-Menashe, D., Woodcock, C. E., Sonnentag, O. & Friedl, M. A. ABoVE: Annual Land Cover in the ABoVE Core Domain from Landsat, 1984–2014 (ORNL DAAC, 2019); https://doi.org/10.3334/ORNLDAAC/1691

  • 62.

    Canadian National Fire Database—Agency Fire Data (Canadian Forest Service, 2002); https://cwfis.cfs.nrcan.gc.ca/ha/nfdb

  • 63.

    Alaskan Large Fire Database (Alaska Interagency Coordination Center, 2002); https://fire.ak.blm.gov/predsvcs/maps.php

  • 64.

    Thornton, M. M. et al. Daymet: Monthly Climate Summaries on a 1-km Grid for North America Version 3 (ORNL DAAC, 2018); https://doi.org/10.3334/ornldaac/1345

  • 65.

    Lumley, T. leaps: Regression Subset Selection Version 3.0. R package (2017).

  • 66.

    Mallows, C. L. Some comments on Cp. Technometrics 42, 87–94 (2000).

    Google Scholar 

  • 67.

    Li, Z., Kurz, W. A., Apps, M. J. & Beukema, S. J. Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP. Can. J. For. Res. 33, 126–136 (2003).

    Article 

    Google Scholar 

  • 68.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).


  • Source: Ecology - nature.com

    Q&A: Vivienne Sze on crossing the hardware-software divide for efficient artificial intelligence

    China’s transition to electric vehicles