Halving carbon emissions from tropical deforestation by 2020 could help bring the international community closer to the agreed goal of <2 degree increase in global average temperature change and is consistent with a target set last year by the governments, corporations, indigenous peoples' organizations and non‐governmental organizations that signed the New York Declaration on Forests (NYDF). We assemble and refine a robust dataset to establish a 2001–2013 benchmark for average annual carbon emissions from gross tropical deforestation at 2.270 Gt CO 2 yr−1. Brazil did not sign the NYDF, yet from 2001 to 2013, Brazil ranks first for both carbon emissions from gross tropical deforestation and reductions in those emissions – its share of the total declined from a peak of 69% in 2003 to a low of 20% in 2012. Indonesia, an NYDF signatory, is the second highest emitter, peaking in 2012 at 0.362 Gt CO 2 yr−1 before declining to 0.205 Gt CO 2 yr−1 in 2013. The other 14 NYDF tropical country signatories were responsible for a combined average of 0.317 Gt CO 2 yr−1, while the other 86 tropical country non‐signatories were responsible for a combined average of 0.688 Gt CO 2 yr−1. We outline two scenarios for achieving the 50% emission reduction target by 2020, both emphasizing the critical role of Brazil and the need to reverse the trends of increasing carbon emissions from gross tropical deforestation in many other tropical countries that, from 2001 to 2013, have largely offset Brazil's reductions. Achieving the target will therefore be challenging, even though it is in the self‐interest of the international community. Conserving rather than cutting down tropical forests requires shifting economic development away from a dependence on natural resource depletion toward recognition of the dependence of human societies on the natural capital that tropical forests represent and the goods and services they provide.
Widespread occurrence of fires in Amazonian forests is known to be associated with extreme droughts, but historical data on the location and extent of forest fires are fundamental to determining the degree to which climate conditions and droughts have affected fire occurrence in the region. We used remote sensing to derive a 23-year time series of annual landscape-level burn scars in a fragmented forest of the eastern Amazon. Our burn scar data set is based on a new routine developed for the Carnegie Landsat Analysis System (CLAS), called CLAS-BURN, to calculate a physically based burn scar index (BSI) with an overall accuracy of 93% (Kappa coefficient 0.84). This index uses sub-pixel cover fractions of photosynthetic vegetation, non-photosynthetic vegetation, and shade/burn scar spectral end members. From 23 consecutive Landsat images processed with the CLAS-BURN algorithm, we quantified fire frequencies, the variation in fire return intervals, and rates of conversion of burned forest to other land uses in a 32 400 km2 area. From 1983 to 2007, 15% of the forest burned; 38% of these burned forests were subsequently deforested, representing 19% of the area cleared during the period of observation. While 72% of the fire-affected forest burned only once during the 23-year study period, 20% burned twice, 6% burned three times, and 2% burned four or more times, with the maximum of seven times. These frequencies suggest that the current fire return interval is 5-11 times more frequent than the estimated natural fire regime. Our results also quantify the substantial influence of climate and extreme droughts caused by a strong El Niño Southern Oscillation (ENSO) on the extent and likelihood of returning forest fires mainly in fragmented landscapes. These results are an important indication of the role of future warmer climate and deforestation in enhancing emissions from more frequently burned forests in the Amazon.
Biomass accumulation in the secondary forests of abandoned pastures and slash-and-burn agricultural fallows is an important but poorly constrained component of the regional carbon budget for the Brazilian Amazon. Using empirical relationships derived from a global analysis, we predicted potential aboveground biomass accumulation (ABA) for the region's regrowth forests based on soil texture and climate data. For regrowth forests on nonsandy soils, the globally derived relationship provided a nearly unbiased linear predictor of Amazonian validation data consisting of 66 stands at seven sites; there was no significant difference between stands that regrew following use as pasture land and those that regrew following slash-and-burn agriculture. For regrowth forests on nonsandy soil, the 1 sigma error range of our ABA model was 58%-171% for the Amazonian validation data. For regrowth for-ests on sandy soils, the validation data were limited to 19 stands at one site, and the globally derived relationship was substantially biased multiplicatively and nonlinearly. Hence we developed a regional refinement by adding to our validation data ABA values from the two Amazonian sites with sandy soil that had previously been included in the global analysis. Based on a conservative jackknife goodness-of-fit assessment (leaving out one site at a time), we calculated a 1 sigma error range of 42%-158% for our sandy soil Amazonian regrowth forest ABA model. We present our predictions of potential regrowth forest ABA as a set of 0.5°resolution maps for the region at 5, 10, and 20 years following abandonment.
[1] Changes in land-use and climate are likely to alter moisture and substrate availability in tropical forest soils, but quantitative assessment of the role of resource constraints as regulators of soil trace gas fluxes is rather limited. The primary objective of this study was to quantify the effects of moisture and substrate availability on soil trace gas fluxes in an Amazonian regrowth forest. We measured the efflux of carbon dioxide (CO 2 ), nitric oxide (NO), nitrous oxide (N 2 O), and methane (CH 4 ) from soil in response to two experimental manipulations. In the first, we increased soil moisture availability during the dry season by irrigation; in the second, we decreased substrate availability by continuous removal of aboveground litter. In the absence of irrigation, soil CO 2 efflux decreased during the dry season while irrigation maintained soil CO 2 efflux levels similar to the wet season. Large variations in soil CO 2 efflux consistent with a significant moisture constraint on respiration were observed in response to soil wet-up and dry-down events. Annual soil C efflux for irrigated plots was 27 and 13% higher than for control plots in 2001 and 2002, respectively. Litter removal significantly reduced soil CO 2 efflux; annual soil C efflux in 2002 was 28% lower for litter removal plots compared to control plots. The annual soil C efflux:litterfall C ratio for the control treatment (4.0-5.2) was consistent with previously reported values for regrowth forests that indicate a relatively large belowground C allocation. In general, fluxes of N 2 O and CH 4 were higher during the wet season and both fluxes increased during dry-season irrigation. There was no seasonal effect on NO fluxes. Litter removal had no significant impact on N oxide or CH 4 emissions. Net soil nitrification did not respond to dry-season irrigation, but was somewhat reduced by litter removal. Overall, these results demonstrate significant soil moisture and substrate constraints on soil trace gas emissions, particularly for CO 2 , and suggest that climate and land-use changes that alter moisture and substrate availability are therefore likely to have an impact on atmosphere chemistry.
Fire slows Amazon forest regrowth DJ Zarin et al. 366www.frontiersinecology.org
Fire slows Amazon forest regrowth DJ Zarin et al. 366www.frontiersinecology.org
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.