Biomass increases go under cover : woody vegetation dynamics in South African rangelands

Woody biomass dynamics are an expression of ecosystem function, yet biomass estimates do not provide information on the spatial distribution of woody vegetation within the vertical vegetation subcanopy. We demonstrate the ability of airborne light detection and ranging (LiDAR) to measure aboveground biomass and subcanopy structure, as an explanatory tool to unravel vegetation dynamics in structurally heterogeneous landscapes. We sampled three communal rangelands in Bushbuckridge, South Africa, utilised by rural communities for fuelwood harvesting. Woody biomass estimates ranged between 9 Mg ha-1 on gabbro geology sites to 27 Mg ha-1 on granitic geology sites. Despite predictions of woodland depletion due to unsustainable fuelwood extraction in previous studies, biomass in all the communal rangelands increased between 2008 and 2012. Annual biomass productivity estimates (10–14% p.a.) were higher than previous estimates of 4% and likely a significant contributor to the previous underestimations of modelled biomass supply. We show that biomass increases are attributable to growth of vegetation <5 m in height, and that, in the high wood extraction rangeland, 79% of the changes in the vertical vegetation subcanopy are gains in the 1-3m height class. The higher the wood extraction pressure on the rangelands, the greater the biomass increases in the low height classes within the subcanopy, likely a strong resprouting response to intensive harvesting. Yet, fuelwood shortages are still occurring, as evidenced by the losses in the tall tree height class in the high extraction rangeland. Loss of large trees and gain in subcanopy shrubs could result in a structurally simple landscape with reduced functional capacity. This research demonstrates that intensive harvesting can, paradoxically, increase biomass and this has implications for the sustainability of ecosystem service provision. The structural implications of biomass increases in communal rangelands could be misinterpreted as woodland recovery in the absence of three-dimensional, subcanopy information.S1 Dataset. Biomass model data. Data include 2012 LiDAR-derived average height and canopy cover extraction metrics, as well as field-work based allometry. Each line item is per 25 m x 25 m grid cell. Metadata are included in the dataset.S2 Dataset. Biomass and subcanopy data. Data include 2008 and 2012 biomass estimates derived from biomass models as well as % subcanopy returns for voxel data for the height class categories: 1-3m, 3-5m, 5-10m and >10m. Each line item is per 25 m x 25 m grid cell. Data are organized per land extraction category into separate worksheets. Metadata are included in the dataset.S3 Dataset. Biomass changes (Mg ha-1) in relation to relative height and canopy cover change. Data include biomass change estimates (2008–2012), percentage height and canopy cover changes for each 25 m x 25 m grid cell. Each height class (relative to height in 2008) are shown on separate worksheets. Metadata are included in the dataset.S1 Fig. Site-specific biomass model residuals. The residual spread demonstrates heteroskedasticity with increasing biomass fitted values for rangelands with a) high, b) intermediate and c) low extraction pressure.S2 Fig. Biomass changes (%) relative to height-specific change in subcanopy returns (%). Height categories are: 1–3 m, 3–5 m, 5–10 m and >10 m.The Carnegie Airborne Observatory (CAO) is made possible by the Avatar Alliance Foundation, Margaret A. Cargill Foundation, John D. and Catherine T. MacArthur Foundation, Grantham Foundation for the Protection of the Environment, W. M. Keck Foundation, Gordon and Betty Moore Foundation, Mary Anne Nyburg Baker and G. Leonard Baker, Jr., and William R. Hearst III. Application of the CAO data in South Africa is made possible through the Andrew Mellon Foundation and the endowment of the Carnegie Institution for Science, the Council for Scientific and Industrial Research (CSIR), and the South African Department of Science and Technology (grant agreement DST/ CON 0119/2010, Earth Observation Application Development in Support of SAEOS). CSIR coauthors are supported by the European Union’s Seventh Framework Programme (FP7/2007-2013, grant agreement n°282621, AGRICAB). PJM acknowledges funding from the National Research Foundation (NRF: SFH1207203615). Additionally, PJM and ETFW acknowledge the DST-NRF Centre of Excellence in Tree Health Biotechnology (CTHB) and, PJM and BFNE, the Applied Centre for Climate and Earth Systems Science (ACCESS). BFNE acknowledges financial support from Exxaro.http://www.plosone.orgam201