Abstract: Microbial-based indicators, such as C and N contents or microbial functions involved in C and N cycles, are currently used to describe the status of soils in disturbed areas. Microbial functions are more accurate indicators but their measurement for studies at the ecosystem level remains problematical because of the huge spatial variability of these processes and, consequently, of the large number of soil samples which must be analyzed. Our goal was to test the capacity of near-infrared reflectance spectroscopy (NIRS) to predict respiration and denitrification but also carbon and nitrogen contents of soils submitted to various procedures of restoration. To achieve this objective, we took advantage of an experiment conducted on a reforestation system established after open-cast gold mining in French Guiana. In this experimental station, plantations of various ages and various soil textures were at our disposal. Our results showed that both plantations and soil texture had a strong impact on the recovery of soil functioning: carbon and nitrogen contents, respiration and denitrification increased with age of plantation and clay content. Calibrations were performed between spectral data and microbial-based indicators using partial least squares regression (PLS). The results showed that C and N contents were accurately predicted. Microbial functions were less precisely predicted with results more accurate on clayey soils than on sandy soils. In clayey soils, perturbed or restored soils and the year of plantation were discriminated very efficiently through principal component analyses of spectral signatures (over 80% of variance explained on the first two axes). Near-infrared spectroscopy may thus be extended to the prediction of functional soil parameters, but the capacity of this method must be strengthened by expending the databases with other soils in other contexts. The possibility of using NIRS provides many opportunities for understanding both the temporal dynamics and the spatial variability of the recovery of key microbial functions during soil restoration. (c) 2007 Elsevier B.V. All rights reserved.

Abstract: MILLET J, PASCAL JP & MET LC. 2010. Effects of disturbance over 60 years on a lowland forest in southern Vietnam. Tropical lowland forests are some of the most threatened in the world and this is particularly the case in Vietnam. This study aimed to identify changes in species composition and forest structure in the Tan Phu lowland forest resulting from disturbance over a 60 year period. Analysis of forest composition and structure rely on data from 25 plots of 0.5-ha size established in a lowland secondary forest. The five forest stands described differed greatly from the three forest stand types described in 1943. Some long-lived shade-tolerant species had been replaced by pioneer species, such as Cratoxylon formosum and Shorea roxburghii. In addition to altering composition, forest disturbances had resulted in large changes in forest structure. While in the past, forest stands had a large number of exploitable trees, current forest stands have few trees in the diameter class > 50 cm and sometimes no trees in the diameter class > 80 cm. This paper provides notable results on forest tree ecology, forest dynamics and on the state of secondary forests in Vietnam. This is particularly important as future goods and services will increasingly have to come from such forests.

Abstract: Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison. We developed a Zero-Inflated Poisson (ZIP) model with a latent correlated spatial structure and compared it with non-spatial ZIP and Poisson models that were either autocorrelated (Spatial Generalized Linear Mixed, SGLM) or not (generalized linear models, GLM). In our spatial models, local density autocorrelation was modeled by a Conditional Auto-Regressive (CAR) process. 13 explicative variables described ecological conditions with respect to topography, disturbance, stand structure and intraspecific processes. Models were applied to six tropical tree species with differing biological attributes: Oxandra asbeckii, Eperua falcata, Eperua grandiflora, Dicorynia guianensis, Qualea rosea, and Tachigali melinonii. We built species-specific models using a simple method of variable selection based on a latent binary indicator. Our spatial models showed a close correlation between observed and estimated densities with site spatial structure being correctly reproduced. By contrast, the non-spatial models showed poor fits. Variable selection highlighted species-specific requirements and susceptibility to local conditions. Model comparison overall showed that the SGLM was the most accurate explanatory and predictive model. Surprisingly, zero-inflated models performed less well. Although the SZIP model was relevant with respect to data distribution, and more flexible with respect to response curves, its model complexity caused marked variability in parameter estimates. In the SUM, the spatial process alone accounted for zero-inflation in the data. A refinement of the hypotheses employed at the process level could compensate for distribution flaws at the data level. This study emphasized the importance of the HBM framework in improving the modelling of density-environment relationships. (C) 2008 Elsevier B.V. All rights reserved.

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: Monitoring the state of small waterbodies and wetlands is very useful in dry regions, because their existence is entirely controlled by the rhythm of local rainfall. On VEGETATION image colour composites, waterbodies and marshy vegetation show up clearly. Yet simple image classification does not yield sufficiently good results because 'spectral signatures' vary significantly together with the ecological conditions of these surfaces. A robust contextual procedure taking into account local contrast was successfully developed and tested. A systematic validation was carried out and a map of waterbodies and wetlands was produced for Burkina Faso and neighbouring regions.