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  5. The importance of distinguishing natural forest regrowth from managed tree systems

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Preprint
English
2024

The importance of distinguishing natural forest regrowth from managed tree systems

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English
2024
DOI: 10.33774/coe-2024-tz347

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Dmitry Schepaschenko
Dmitry Schepaschenko

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Xueyuan Gao
Peter B. Reich
Jeffrey R. Vincent
+11 more

Abstract

Nature-based climate solutions, such as forest landscape restoration, offer a promising approach to mitigate the effects of global climate change, conserve biodiversity, and enhance rural livelihoods. Heinrich et al. (Nature, 2023) used satellite observation products to assess rates and drivers of aboveground carbon accumulation in tropical recovering forests, which is knowledge essential for understanding their climate mitigation potential. They used a tropical moist forest cover change dataset developed by the European Commission’s Joint Research Centre (JRC) to identify tree cover gains on former agricultural lands, and they referred to these gains as “secondary forests” assuming that all the gains were natural forest regrowth. However, tropical tree cover gains on former agricultural lands also include managed tree systems, e.g., timber plantations, oil palm plantations, and agroforestry. Ignoring the contribution of gains due to managed tree systems likely leads to a significant underestimation of the impact of management practices on tropical carbon sequestration; such impacts might either increase or decrease the rate of carbon sequestration by tropical tree-based systems on former agricultural lands, compared with natural forest recovery solely.

How to cite this publication

Xueyuan Gao, Peter B. Reich, Jeffrey R. Vincent, Matthew D. Potts, Matthew C. Hansen, Martin Jung, Pedro H. S. Brancalion, Steffen Fritz, Dmitry Schepaschenko, Robin L. Chazdon, María Uriarte, Trevor F. Keenan, Myroslava Lesiv, Dongdong Wang (2024). The importance of distinguishing natural forest regrowth from managed tree systems. , DOI: 10.33774/coe-2024-tz347.

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Publication Details

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Preprint

Year

2024

Authors

14

Datasets

0

Total Files

0

Language

English

DOI

10.33774/coe-2024-tz347

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