First Ever Amazonian Carbon Recovery Mapping Published

The Amazon is the world’s biggest forest, and it holds around a third of the carbon stored in all the planet’s  forests. Logging releases a great deal of stored carbon into the atmosphere, to be recovered again by by surviving trees and new growth.

Now the very first maps revealing post-commercial logging carbon recovery in the Amazon’s rainforests have been published in the journal eLife. And it looks like trees that survive logging might be better at storing carbon than young trees that fill the spaces left behind. This is the first such study to be carried out Amazon-wide.

Predicting post-logging carbon sequestration levels across the Amazon

A team from the Tropical Managed Forest Observatory have modelled the way different forest environments impact carbon changes in surviving trees and new trees grown post-logging, examining data from 133 permanent forest plots in 13 experimentally disturbed sites, looking at regional differences in climate, soils, and the surface biomass. These were linked with changes in carbon stocks caused by both surviving and new trees in an attempt to predict carbon recovery potential right across the Amazon.

The Guiana Shield wins the carbon stakes

The results reveal carbon recovery is highest in the Guiana Shield, an area of north east South America, and the western Amazonian forests, where surviving trees have sucked up a lot more carbon than they have in the south of the region. The Guiana Shield forests are denser, living off nutrient-poor soils, but in the south  high seasonal water stress is the main barrier to carbon recovery. As a general rule stress-tolerant trees are not as good at sucking up carbon, which could explain the difference.

We can’t just rely on new growth to suck up carbon

The results of the research hint that it’s unwise to rely on new growth to store carbon in forests disturbed by logging, since newer growth is very vulnerable to water stress. The trees that survive logging seem to be a better bet, which delivers clues about how to predict a forest’s responses to carbon loss from climate change.


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