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1.
Nat Commun ; 12(1): 1785, 2021 03 19.
Article in English | MEDLINE | ID: mdl-33741981

ABSTRACT

Tropical secondary forests sequester carbon up to 20 times faster than old-growth forests. This rate does not capture spatial regrowth patterns due to environmental and disturbance drivers. Here we quantify the influence of such drivers on the rate and spatial patterns of regrowth in the Brazilian Amazon using satellite data. Carbon sequestration rates of young secondary forests (<20 years) in the west are ~60% higher (3.0 ± 1.0 Mg C ha-1 yr-1) compared to those in the east (1.3 ± 0.3 Mg C ha-1 yr-1). Disturbances reduce regrowth rates by 8-55%. The 2017 secondary forest carbon stock, of 294 Tg C, could be 8% higher by avoiding fires and repeated deforestation. Maintaining the 2017 secondary forest area has the potential to accumulate ~19.0 Tg C yr-1 until 2030, contributing ~5.5% to Brazil's 2030 net emissions reduction target. Implementing legal mechanisms to protect and expand secondary forests whilst supporting old-growth conservation is, therefore, key to realising their potential as a nature-based climate solution.


Subject(s)
Carbon Sequestration , Carbon/metabolism , Climate Change , Forests , Tropical Climate , Algorithms , Biomass , Brazil , Conservation of Natural Resources/methods , Ecosystem , Fires , Forestry , Geography , Models, Theoretical , Satellite Imagery/methods , Trees/growth & development , Trees/metabolism
2.
Sci Data ; 7(1): 269, 2020 08 14.
Article in English | MEDLINE | ID: mdl-32796858

ABSTRACT

The restoration and reforestation of 12 million hectares of forests by 2030 are amongst the leading mitigation strategies for reducing carbon emissions within the Brazilian Nationally Determined Contribution targets assumed under the Paris Agreement. Understanding the dynamics of forest cover, which steeply decreased between 1985 and 2018 throughout Brazil, is essential for estimating the global carbon balance and quantifying the provision of ecosystem services. To know the long-term increment, extent, and age of secondary forests is crucial; however, these variables are yet poorly quantified. Here we developed a 30-m spatial resolution dataset of the annual increment, extent, and age of secondary forests for Brazil over the 1986-2018 period. Land-use and land-cover maps from MapBiomas Project (Collection 4.1) were used as input data for our algorithm, implemented in the Google Earth Engine platform. This dataset provides critical spatially explicit information for supporting carbon emissions reduction, biodiversity, and restoration policies, enabling environmental science applications, territorial planning, and subsidizing environmental law enforcement.

3.
Sci Data ; 7(1): 287, 2020 08 28.
Article in English | MEDLINE | ID: mdl-32859937

ABSTRACT

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

4.
Nat Commun ; 8: 15519, 2017 05 30.
Article in English | MEDLINE | ID: mdl-28555627

ABSTRACT

A bimodal distribution of tropical tree cover at intermediate precipitation levels has been presented as evidence of fire-induced bistability. Here we subdivide satellite vegetation data into those from human-unaffected areas and those from regions close to human-cultivated zones. Bimodality is found to be almost absent in the unaffected regions, whereas it is significantly enhanced close to cultivated zones. Assuming higher logging rates closer to cultivated zones and spatial diffusion of fire, our spatiotemporal mathematical model reproduces these patterns. Given a gradient of climatic and edaphic factors, rather than bistability there is a predictable spatial boundary, a Maxwell point, that separates regions where forest and savanna states are naturally selected. While bimodality can hence be explained by anthropogenic edge effects and natural spatial heterogeneity, a narrow range of bimodality remaining in the human-unaffected data indicates that there is still bistability, although on smaller scales than claimed previously.


Subject(s)
Climate , Ecology , Forests , Models, Theoretical , Brazil , Computer Simulation , Geography , Grassland , Greenhouse Gases , Humans , Rain , Regression Analysis , Software , Soil , Trees
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