Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

Abstract

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year−1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year−1. Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement’s bold targets.

Drivers of carbon stocks in forest edges across Europe

“…These patterns strongly contrast with recent findings from the subtropics (Saeed et al, 2019) and tropics (Smith et al, 2018;Silva Junior et al, 2020). In the tropical forest biome, after edge creation, the Cstock in the remaining forest patches is negatively affected due to altered microclimate conditions (i.e.…”

Spatial-Temporal Analysis of the Forest Fragments Surrounding a Conservation Unit in the Southern Region of Brazil

“…It is important to emphasize that most forests in Brazil are under degradation [18,19] due of the deforestation processes, and because of this, the edge effect causes carbon losses, mainly in the Amazonia tropical forest [20]. So, what our study showed is an exception to what have been registered in the country.…”

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

“…It is likely that we are missing losses driven by degradation, re-clearance events, and edge effects (e.g., Yang et al, 2020)(e.g. Yang et al, 2020) that are not accounted for in existing EO datasets, such as GFW, that are used to drive disturbance in our models (Milodowski et al, 2017;Silva Junior et al, 2020 these disturbance events would lead to overestimation of long term accumulation of woody carbon, consistent with the likely overestimate of net carbon uptake estimated by the DALEC models already discussed in comparison with CTE. Moreover, 520 improved disturbance drivers can add additional constraint to the C-cycle, potentially reducing uncertainty and refining our best estimates of key ecosystem traits as has previously been demonstrated due to the inclusion of fire disturbance information (Exbrayat et al, 2018b).…”