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Large-scale variation in boreal and temperate forest carbon turnover rate is related to climate

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Thurner,  Martin
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Carvalhais,  Nuno
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Forkel,  Matthias
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry , Max Planck Society;

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Citation

Thurner, M., Beer, C., Carvalhais, N., Forkel, M., Santoro, M., Tum, M., et al. (2016). Large-scale variation in boreal and temperate forest carbon turnover rate is related to climate. Geophysical Research Letters, 43(9), 4576-4585. doi:10.1002/2016GL068794.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-3F43-1
Abstract
Vegetation carbon turnover processes in forest ecosystems and their dominant drivers are far from being understood at a broader scale. Many of these turnover processes act on long time-scales and include a lateral dimension and thus can hardly be investigated by plot-level studies alone. Making use of remote sensing based products of net primary productivity (NPP) and biomass, here we show that spatial gradients of carbon turnover rate (k) in Northern Hemisphere boreal and temperate forests are explained by different climate-related processes depending on the ecosystem. k is related to frost damage effects and the trade-off between growth and frost adaptation in boreal forests, while drought stress and climate effects on insects and pathogens can explain an elevated k in temperate forests. By identifying relevant processes underlying broad-scale patterns in k, we provide the basis for a detailed exploration of these mechanisms in field studies, and ultimately the improvement of their representations in global vegetation models (GVMs).