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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
Published on: June 24, 2019
Global covariation of forest age transitions with the net carbon balance
Simon Besnard1, Viola H A Heinrich2,3, Nuno Carvalhais4,5,6
1GFZ Helmholtz Centre for Geosciences, Potsdam, Germany. simon.besnard@gfz.de.
Abstract:
Forest age transitions are critical in shaping the global carbon balance, yet their influence on carbon stocks and fluxes remains poorly quantified. Here we analyse global forest age dynamics from 2010 to 2020 using the Global Age Mapping Integration v2.0 dataset, alongside satellite-derived aboveground carbon (AGC) and atmospheric inversion-derived net CO2 flux data. We reveal widespread declines in forest age across the Amazon, Congo Basin, Southeast Asia and parts of Siberia, primarily driven by stand-replacing disturbances such as fire and harvest, leading to the replacement of older forests by younger stands. Meanwhile, forests in China, Europe and North America experienced net ageing. Globally, stand replacement resulted in substantial AGC losses, with old forests (>200 years, ~98.0 MgC ha-1) transitioning to younger, carbon-poor stands (<20 years, ~43.5 MgC ha-1), leading to a net AGC loss of ~0.14 PgC per year. Despite this, regions with high rates of young stands replacing old forests exhibited a temporary strengthening of the carbon sink, driven by the rapid regrowth of these young stands. Crucially, these young forests do not compensate for the long-term carbon storage of old forests. Our findings underscore the importance of protecting old forests while optimizing forest management strategies to maximize carbon gains and enhance climate mitigation.
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In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

