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Tropical forests post-logging are a persistent net carbon source to the atmosphere.

Maria B MillsYadvinder MalhiRobert M EwersLip Khoon KhoYit Arn TehSabine BothDavid F R P BurslemNoreen MajalapReuben NilusWalter Huaraca HuascoRudi S Cruz ChinoMilenka M PillcoEdgar C TurnerGlen ReynoldsTerhi Riutta
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
Logged and structurally degraded tropical forests are fast becoming one of the most prevalent land-use types throughout the tropics and are routinely assumed to be a net carbon sink because they experience rapid rates of tree regrowth. Yet this assumption is based on forest biomass inventories that record carbon stock recovery but fail to account for the simultaneous losses of carbon from soil and necromass. Here, we used forest plots and an eddy covariance tower to quantify and partition net ecosystem CO 2 exchange in Malaysian Borneo, a region that is a hot spot for deforestation and forest degradation. Our data represent the complete carbon budget for tropical forests measured throughout a logging event and subsequent recovery and found that they constitute a substantial and persistent net carbon source. Consistent with existing literature, our study showed a significantly greater woody biomass gain across moderately and heavily logged forests compared with unlogged forests, but this was counteracted by much larger carbon losses from soil organic matter and deadwood in logged forests. We estimate an average carbon source of 1.75 ± 0.94 Mg C ha -1 yr -1 within moderately logged plots and 5.23 ± 1.23 Mg C ha - 1 yr - 1 in unsustainably logged and severely degraded plots, with emissions continuing at these rates for at least one-decade post-logging. Our data directly contradict the default assumption that recovering logged and degraded tropical forests are net carbon sinks, implying the amount of carbon being sequestered across the world's tropical forests may be considerably lower than currently estimated.
Keyphrases
  • climate change
  • big data
  • machine learning
  • deep learning
  • risk assessment
  • organic matter
  • wastewater treatment
  • artificial intelligence
  • heavy metals
  • quantum dots
  • sensitive detection
  • data analysis