Abstract: Illicit drug consumption can be estimated from drug target residue (DTR) in wastewater, with the reliability of results being partly linked to DTR stability in the sewage network. However, wastewater temperature and pH drive the stability of molecules and, in this context, tropical conditions must be studied to specify the impact of residence time in the sewage network on DTR degradation. Warmth enhances biotic and abiotic processes such as degradation, leading to a decrease in oxygen content, and consequently, early diagenesis conditions in wastewater. In this study, we conduct laboratory studies under acidic pH and high temperature (30 °C) conditions to determine the degradation half-lives of cocaine (COC), tetrahydrocannabinol, and heroine targets, allowing COC/benzoylecgonine (BZE) ratio variations to be predicted in sewage networks. A rapid COC degradation is observed, as already reported in the literature but without a short-term significant difference between 20 °C and 30 °C. Acidic pH seems to prevent degradation. Thus, theoretically, the use of COC as DTR is only reliable in acidic conditions, with the decrease in COC concentration being 6% at 8 h, but over 40% in other conditions. By contrast, the use of BZE as DTR to estimate COC consumption, which is performed in practice, can be undertaken with the same back-calculation equation as used in temperate countries. However, 11-nor-delta-9-carboxytetrahydrocannabinol stability is more influenced by high temperature: concentration levels after 24 h are 20% lower at 30 °C than at 20 °C, corresponding to a 20% and 40% decrease, respectively. Based on a mean residence time of 8 h, underestimated cannabis consumption is close to 15% in tropical contexts, which is double that of temperate areas. © 2017 Elsevier B.V.

Abstract: Molecular analysis by parallel tandem mass spectrometry (MS/MS) imaging contributes to the in situ characterization of biosynthetic intermediates which is crucial for deciphering the metabolic pathways in living organisms. We report the first use of TOF-SIMS MS/MS imaging for the cellular localization and characterization of biosynthetic intermediates of bioactive γ-lactones rubrynolide and rubrenolide in the Amazonian tree Sextonia rubra (Lauraceae). Five γ-lactones, including previously reported rubrynolide and rubrenolide, were isolated using a conventional approach and their structural characterization and localization at a lateral resolution of ~400 nm was later achieved using TOF-SIMS MS/MS imaging analysis. 2D/3D MS imaging at subcellular level reveals that putative biosynthetic γ-lactones intermediates are localized in the same cell types (ray parenchyma cells and oil cells) as rubrynolide and rubrenolide. Consequently, a revised metabolic pathway of rubrynolide was proposed, which involves the reaction between 2-hydroxysuccinic acid and 3-oxotetradecanoic acid, contrary to previous studies suggesting a single polyketide precursor. Our results provide insights into plant metabolite production in wood tissues and, overall, demonstrate that combining high spatial resolution TOF-SIMS imaging and MS/MS structural characterization offers new opportunities for studying molecular and cellular biochemistry in plants. © 2019, The Author(s).

Abstract: We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure. © 2012 Elsevier Inc.

Abstract: Essential oil from the Amazonian rosewood tree (Aniba rosaeodora Ducke) is valued as an important aromatic ingredient in luxury perfumes. Due to over-harvesting in recent decades, rosewood is now listed as an endangered species. Rosewood tree planting is now considered a viable alternative to logging as it can support both reforestation and sustainable agriculture thanks to sales of the essential oil extracted. We planted 605 rosewood trees in French Guiana from two seeds of local provenance, in a 5 445 m2 plot surrounded by primary forest. Nine years after planting, we assessed the effect of the position of the tree relative to the surrounding forest and of the seed provenance on dendrometric traits (height, circumference, above ground woody biomass) and hence on the yield of essential oil. Measurements were made on 99 trees. Average growth rates for the young trees were 0.7 m/year in height, 2.5 cm/year in stem circumference and 990.5 kg dry mass/ha/year in aboveground biomass, while essential oil yields ranged from 0.6% to 3.6% with a mean of 2.1%. The position of the tree relative to the surrounding forest was the main factor affecting tree growth and essential oil production: trees located close to the surrounding forest were significantly smaller and accumulated less essential oil due to the reduced availability of light. Seed provenance had less effect on dendrometric traits and essential oil yields. In conclusion, although planting practices will need to be adapted to avoid the edge effects of proximity to the forest, short-rotation cultivation of rosewood trees could be the optimum and most economically attractive system for the production of essential oil.

Abstract: Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2 = 0.54, RMSE = 48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50 Mg/ha and R2 = 0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values. © 2016 Elsevier B.V.