Quantifying Long-Term Changes in Carbon Stocks and Forest Structure from Amazon Forest Degradation

Despite sustained declines in Amazon deforestation, forest degradation from logging and firecontinues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arcof deforestation. Limited data on the magnitude of carbon losses and rates of carbon recoveryfollowing forest degradation have hindered carbon accounting efforts and contributed to incompletenational reporting to reduce emissions from deforestation and forest degradation (REDD+). Wecombined annual time series of Landsat imagery and high-density airborne lidar data to characterizethe variability, magnitude, and persistence of Amazon forest degradation impacts on abovegroundcarbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of thecarbon stocks in intact forests, and differences persisted even after 15 years of regrowth. Incomparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a historyof one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,selective logging before burning did not explain additional variability in modeled ACD loss andrecovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure thatcan aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recoveredfaster than attributes of canopy structure that are critical for biodiversity in tropical forests, includingthe abundance of tall trees. We provide the first comprehensive look-up table of emissions factors forspecific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon lossand recovery trajectories provide an important foundation for assessing the long-term contributionsfrom forest degradation to regional carbon cycling and advance our understanding of the currentstate of frontier forests

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 67 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of -4.14 +/- 0.76 MgC/hay. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data

A review on the recent advances in HPLC, UHPLC and UPLC analyses of naturally occurring cannabinoids (2010-2019)

Introduction: Organic molecules that bind to cannabinoid receptors are called cannabinoids, and they have similar pharmacological properties like the plant, Cannabis sativa L. Hyphenated liquid chromatography (LC), incorporating high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC, also known as ultra high performance liquid chromatography, UHPLC), usually coupled to a UV, UV-PDA or MS detector, has become a popular analytical tool for the analysis of naturally occurring cannabinoids in various matrices. Objective: To review literature on the use of various LC-based analytical methods for the analysis of naturally occurring cannabinoids published since 2010. Methodology: A comprehensive literature search was performed utilizing several databases, like Web of Knowledge, PubMed and Google Scholar, and other relevant published materials including published books. The keywords used, in various combinations, with cannabinoids being present in all combinations, in the search were Cannabis, hemp, cannabinoids, Cannabis sativa, marijuana, analysis, HPLC, UHPLC, UPLC, quantitative, qualitative and quality control. Results: Since 2010, several LC methods for the analysis of naturally occurring cannabinoids have been reported. While simple HPLC-UV or HPLC-UV-PDA-based methods were common in cannabinoids analysis, HPLC-MS, HPLC-MS/MS, UPLC (or UHPLC)-UV-PDA, UPLC (or UHPLC)-MS and UPLC (or UHPLC)-MS/MS, were also used frequently. Applications of mathematical and computational models for optimization of different protocols were observed, and pre-analyses included various environmentally friendly extraction protocols. Conclusions: LC-based analysis of naturally occurring cannabinoids has dominated the cannabinoids analysis during the last ten years, and UPLC and UHPLC methods have been shown to be superior to conventional HPLC methods

Z boson production in Pb+Pb collisions at √Snn = 5.02 TeV measured by the ATLAS experiment

The production yield of Z bosons is measured in the electron and muon decay channels in Pb+Pb collisions at √S = 5.02 TeV with the ATLAS detector. Data from the 2015 LHC run corresponding to an integrated luminosity of 0.49 nb are used for the analysis. The Z boson yield, normalised by the total number of minimum-bias events and the mean nuclear thickness function, is measured as a function of dilepton rapidity and event centrality. The measurements in Pb+Pb collisions are compared with similar measurements made in proton-proton collisions at the same centre-of-mass energy. The nuclear modification factor is found to be consistent with unity for all centrality intervals. The results are compared with theoretical predictions obtained at next-to-leading order using nucleon and nuclear parton distribution functions. The normalised Z boson yields in Pb+Pb collisions lie 1-3σ above the predictions. The nuclear modification factor measured as a function of rapidity agrees with unity and is consistent with a next-to-leading-order QCD calculation including the isospin effect. nn -