Road expansion and persistence in forests of the Congo Basin

ISSN:2398-962

Providing low-budget estimations of carbon sequestration and greenhouse gas emissions in agricultural wetlands

The conversion of wetlands to agriculture through drainage and flooding, and the burning of wetland areas for agriculture have important implications for greenhouse gas (GHG) production and changing carbon stocks. However, the estimation of net GHG changes from mitigation practices in agricultural wetlands is complex compared to dryland crops. Agricultural wetlands have more complicated carbon and nitrogen cycles with both above- and below-ground processes and export of carbon via vertical and horizontal movement of water through the wetland. This letter reviews current research methodologies in estimating greenhouse gas production and provides guidance on the provision of robust estimates of carbon sequestration and greenhouse gas emissions in agricultural wetlands through the use of low cost reliable and sustainable measurement, modelling and remote sensing applications. The guidance is highly applicable to, and aimed at, wetlands such as those in the tropics and sub-tropics, where complex research infrastructure may not exist, or agricultural wetlands located in remote regions, where frequent visits by monitoring scientists prove difficult. In conclusion, the proposed measurement-modelling approach provides guidance on an affordable solution for mitigation and for investigating the consequences of wetland agricultural practice on GHG production, ecological resilience and possible changes to agricultural yields, variety choice and farming practice

National-scale estimation of gross forest aboveground carbon loss: a case study of the Democratic Republic of the Congo

Recent advances in remote sensing enable the mapping and monitoring of carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where national forest inventories (NFI) are either non-existent or out of date. Here we demonstrate a method for estimating national-scale gross forest aboveground carbon (AGC) loss and associated uncertainties using remotely sensed-derived forest cover loss and biomass carbon density data. Lidar data were used as a surrogate for NFI plot measurements to estimate carbon stocks and AGC loss based on forest type and activity data derived using time-series multispectral imagery. Specifically, DRC forest type and loss from the FACET (Forêts d’Afrique Centrale Evaluées par Télédétection) product, created using Landsat data, were related to carbon data derived from the Geoscience Laser Altimeter System (GLAS). Validation data for FACET forest area loss were created at a 30-m spatial resolution and compared to the 60-m spatial resolution FACET map. We produced two gross AGC loss estimates for the DRC for the last decade (2000–2010): a map-scale estimate (53.3 ± 9.8 Tg C yr ^−1 ) accounting for whole-pixel classification errors in the 60-m resolution FACET forest cover change product, and a sub-grid estimate (72.1 ± 12.7 Tg C yr ^−1 ) that took into account 60-m cells that experienced partial forest loss. Our sub-grid forest cover and AGC loss estimates, which included smaller-scale forest disturbances, exceed published assessments. Results raise the issue of scale in forest cover change mapping and validation, and subsequent impacts on remotely sensed carbon stock change estimation, particularly for smallholder dominated systems such as the DRC

A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nature Clim

When forests are cleared, carbon stored above and below ground in leaves, branches, stems and roots is released to the atmosphere. As a consequence, forest clearing, especially in the tropics, is a major source of CO 2 to the atmosphere. Although the proportion of carbon stored in forests comprises 70-80% of total terrestrial carbon 5 , the spatial and temporal variability in carbon storage is substantia

Answering a century old riddle: brachydactyly type A1

In 1903, Farabee analyzed the heredity of the human digital malformation, brachydactyly, the first recorded disorder of the autosomal dominant Mendelian trait. In 1951, Bell classified this type of brachydactyly as type A1 (BDA1). Over 100 cases from different ethnic groups have so far been reported. However, the real breakthrough in identifying the cause of BDA1 has only taken place in the last few years with the progress of the mapping and identification of one of the genes responsible for this disorder, thus providing an answer for a century old riddle. In this article, we attempt to review the current state of knowledge on the genetic features of BDA1 with its century-old history and signalling pathway of IHH, and also discuss genotype-phenotype correlation not only of BDA1, but also of all types of brachydactyly

Detecting vulnerability of humid tropical forests to multiple stressors

Humid tropical forests play a dominant role in the functioning of Earth but are under increasing threat from changes in land use and climate. How forest vulnerability varies across space and time and what level of stress forests can tolerate before facing a tipping point are poorly understood. Here, we develop a tropical forest vulnerability index (TFVI) to detect and evaluate the vulnerability of global tropical forests to threats across space and time. We show that climate change together with land-use change have slowed the recovery rate of forest carbon cycling. Temporal autocorrelation, as an indicator of this slow recovery, increases substantially for above-ground biomass, gross primary production, and evapotranspiration when climate stress reaches a critical level. Forests in the Americas exhibit extensive vulnerability to these stressors, while in Africa, forests show relative resilience to climate, and in Asia reveal more vulnerability to land use and fragmentation. TFVI can systematically track the response of tropical forests to multiple stressors and provide early-warning signals for regions undergoing critical transitions

Observation of WWW Production in pp Collisions at √s = 13 TeV with the ATLAS Detector

This Letter reports the observation of W W W production and a measurement of its cross section using 139     fb − 1 of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from W W W production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive W W W production cross section is measured to be 820 ± 100   ( stat ) ± 80   ( syst )     fb , approximately 2.6 standard deviations from the predicted cross section of 511 ± 18     fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy