in

Woody-biomass projections and drivers of change in sub-Saharan Africa

  • 1.

    Davis, S. J. & Caldeira, K. Consumption-based accounting of CO2 emissions. Proc. Natl Acad. Sci. USA 107, 5687–5692 (2010).

    CAS 
    Article 

    Google Scholar 

  • 2.

    Wiedmann, T. O. et al. The material footprint of nations. Proc. Natl Acad. Sci. USA 112, 6271–6276 (2015).

    CAS 
    Article 

    Google Scholar 

  • 3.

    Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    CAS 
    Article 

    Google Scholar 

  • 4.

    Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).

    Article 

    Google Scholar 

  • 5.

    Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, 846–849 (2005).

    CAS 
    Article 

    Google Scholar 

  • 6.

    Bond, W. J. & Keane, R. E. Fires, Ecological Effects of. In Reference Module in Life Sciences (Elsevier, 2017); https://doi.org/10.1016/B978-0-12-809633-8.02098-7

  • 7.

    Valentini, R. et al. A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities. Biogeosciences 11, 381–407 (2014).

    Article 
    CAS 

    Google Scholar 

  • 8.

    Williams, C. A. et al. Africa and the global carbon cycle. Carbon Balance Manag. 2, 3 (2007).

    Article 
    CAS 

    Google Scholar 

  • 9.

    Hanan, N. P. Agroforestry in the Sahel. Nat. Geosci. 11, 296–297 (2018).

    Article 
    CAS 

    Google Scholar 

  • 10.

    Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).

    Article 

    Google Scholar 

  • 11.

    Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).

    CAS 
    Article 

    Google Scholar 

  • 12.

    Feng, S. & Fu, Q. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13, 10081–10094 (2013).

    CAS 
    Article 

    Google Scholar 

  • 13.

    Anchang, J. Y. et al. Trends in woody and herbaceous vegetation in the savannas of West Africa. Remote Sens. 11, 576 (2019).

    Article 

    Google Scholar 

  • 14.

    Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M. & McVicar, T. R. Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10, 6657–6676 (2013).

    Article 

    Google Scholar 

  • 15.

    Kaptué, A. T., Prihodko, L. & Hanan, N. P. On regreening and degradation in Sahelian watersheds. Proc. Natl Acad. Sci. USA 112, 12133–12138 (2015).

    Article 
    CAS 

    Google Scholar 

  • 16.

    Schneider, S. H. The greenhouse effect: science and policy. Science 243, 771–781 (1989).

    CAS 
    Article 

    Google Scholar 

  • 17.

    Walsh, J. et al. Climate Change Impacts in the United States: The Third National Climate Assessment Ch. 2 (US Global Change Research Program, 2014); https://doi.org/10.7930/J0KW5CXT

  • 18.

    Filatova, T., Polhill, J. G. & van Ewijk, S. Regime shifts in coupled socio-environmental systems: review of modelling challenges and approaches. Environ. Model. Softw. 75, 333–347 (2016).

    Article 

    Google Scholar 

  • 19.

    Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).

    CAS 
    Article 

    Google Scholar 

  • 20.

    Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).

    CAS 
    Article 

    Google Scholar 

  • 21.

    Keys, P. W. et al. Anthropocene risk. Nat. Sustain. 2, 667–673 (2019).

    Article 

    Google Scholar 

  • 22.

    Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).

    Article 
    CAS 

    Google Scholar 

  • 23.

    Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).

    Article 
    CAS 

    Google Scholar 

  • 24.

    Hanan, N. P., Prihodko, L., Ross, C. W., Bucini, G. & Tredennick, A. T. Gridded Estimates of Woody Cover and Biomass across Sub-Saharan Africa, 2000-2004 (ORNL DAAC, 2020); https://doi.org/10.3334/ORNLDAAC/1777

  • 25.

    Bouvet, A. et al. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR. Remote Sens. Environ. 206, 156–173 (2018).

    Article 

    Google Scholar 

  • 26.

    Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Article 

    Google Scholar 

  • 27.

    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    CAS 
    Article 

    Google Scholar 

  • 28.

    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    CAS 
    Article 

    Google Scholar 

  • 29.

    Anchang, J. Y. et al. Toward operational mapping of woody canopy cover in tropical savannas using Google Earth Engine. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2020.00004 (2020).

  • 30.

    Kahiu, M. N. & Hanan, N. P. Fire in sub-Saharan Africa: the fuel, cure and connectivity hypothesis. Glob. Ecol. Biogeogr. 27, 946–957 (2018).

    Article 

    Google Scholar 

  • 31.

    Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).

    Article 

    Google Scholar 

  • 32.

    Ross, C. W. et al. HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling. Sci. Data 5, 180091 (2018).

    Article 

    Google Scholar 

  • 33.

    Lüdeke, M. K. B., Moldenhauer, O. & Petschel-Held, G. Rural poverty driven soil degradation under climate change: the sensitivity of the disposition towards the Sahel Syndrome with respect to climate. Environ. Model. Assess. 4, 315–326 (1999).

    Article 

    Google Scholar 

  • 34.

    Hansfort, S. L. & Mertz, O. Challenging the woodfuel crisis in West African woodlands. Hum. Ecol. 39, 583 (2011).

    Article 

    Google Scholar 

  • 35.

    Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).

    CAS 
    Article 

    Google Scholar 

  • 36.

    Wei, F. et al. Nonlinear dynamics of fires in Africa over recent decades controlled by precipitation. Glob. Change Biol. 26, 4495–4505 (2020).

    Article 

    Google Scholar 

  • 37.

    Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).

    Article 

    Google Scholar 

  • 38.

    Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article 

    Google Scholar 

  • 39.

    Potapov, P. et al. Mapping the World’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 2 (2008).

    Article 

    Google Scholar 

  • 40.

    Herold, M., Mayaux, P., Woodcock, C. E., Baccini, A. & Schmullius, C. Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sens. Environ. 112, 2538–2556 (2008).

    Article 

    Google Scholar 

  • 41.

    Martens, C. et al. Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies. Glob. Change Biol. 27, 340–358 (2021).

    Article 

    Google Scholar 

  • 42.

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

    Article 
    CAS 

    Google Scholar 

  • 43.

    Reich, P. B., Hobbie, S. E. & Lee, T. D. Plant growth enhancement by elevated CO2 eliminated by joint water and nitrogen limitation. Nat. Geosci. 7, 920–924 (2014).

    CAS 
    Article 

    Google Scholar 

  • 44.

    Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).

    CAS 
    Article 

    Google Scholar 

  • 45.

    Körner, C. A matter of tree longevity. Science 355, 130–131 (2017).

    Article 

    Google Scholar 

  • 46.

    Olson, D. M. & Dinerstein, E. The Global 200: priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 89, 199–224 (2002).

    Article 

    Google Scholar 

  • 47.

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

  • 48.

    Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  • 49.

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

    Article 

    Google Scholar 

  • 50.

    Massey, F. J. The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78 (1951).

    Article 

    Google Scholar 

  • 51.

    Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe: version 4: data grid (CGIAR Consortium for Spatial Information, 2008).

  • 52.

    Ross, C. W. et al. Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/1566

  • 53.

    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. https://doi.org/10.1029/2011JG001708 (2011).

  • 54.

    Jucker, T. et al. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob. Change Biol. 23, 177–190 (2017).

    Article 

    Google Scholar 

  • 55.

    Sanderson, E. W. et al. The human footprint and the last of the wild. BioScience 52, 891–904 (2002).

    Article 

    Google Scholar 

  • 56.

    Molnar, C., Bischl, B. & Casalicchio, G. iml: an R package for interpretable machine learning. J. Open Source Softw. 3, 786 (2018).

    Article 

    Google Scholar 

  • 57.

    Wickham, H. tidyverse: Easily Install and Load the ‘Tidyverse’ (CRAN, 2017).

  • 58.

    Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling (CRAN, 2016).

  • 59.

    Perpiñán, O. & Hijmans, R. rasterVis (CRAN, 2018).

  • 60.

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

  • 61.

    Zeileis, A. et al. colorspace: A toolbox for manipulating and assessing colors and palettes. J. Stat. Soft. https://doi.org/10.18637/jss.v096.i01 (2020).

  • 62.

    Neuwirth, E. RColorBrewer: ColorBrewer Palettes (CRAN, 2014).

  • 63.

    Auguie, B. gridExtra: Miscellaneous Functions for ‘Grid’ Graphics (CRAN, 2017).

  • 64.

    Pebesma, E. Simple features for R: standardized support for spatial vector data. R J. 10, 439–446 (2018).

    Article 

    Google Scholar 

  • 65.

    Ross, C. W., Hanan, N. P. & Prihodko, L. Prediction Maps: Woody-Biomass Projections and Drivers of Change in Sub-Saharan Africa (Figshare, 2021); https://doi.org/10.6084/M9.FIGSHARE.14150210.V2

  • 66.

    Ross, C. W. R Code for Woody-Biomass Projections and Drivers of Change in Sub-Saharan Africa (Figshare, 2021); https://doi.org/10.6084/M9.FIGSHARE.14143799.V1


  • Source: Ecology - nature.com

    Innovations in water accessibility

    Changes in soil water holding capacity and water availability following vegetation restoration on the Chinese Loess Plateau