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BackgroundAcross the tropics, the share of secondary versus primary forests is strongly increasing. The high rate of biomass accumulation during this secondary succession relies on the availability of essential nutrients, such as nitrogen (N). Nitrogen primarily limits many young secondary forests in the tropics. However, recent studies have shown that forests of the Congo basin are subject to high inputs of atmospheric N deposition, potentially alleviating this N limitation in early succession. MethodsTo address this hypothesis, we assessed the N status along a successional gradient of secondary forests in the Congo basin. In a set-up of 18 plots implemented along six successional stages, we quantified year-round N deposition, N leaching, N2O emission and the N flux of litterfall and fine root assimilation. Additionally, we determined the N content and C:N stoichiometry for canopy leaves, fine roots, and litter, as well as δ15N of canopy leaves. ResultsWe confirmed that these forests receive high amounts of atmospheric N deposition, with an increasing deposition as forest succession proceeds. Additionally, we noted lower C:N ratios, and higher N leaching losses, N2O emission, and foliar δ15N in older secondary forest (60 years). In contrast, higher foliar, litter and root C:N ratios, and lower foliar δ15N, N leaching, and N2O emission in young (< 20 years) secondary forest were observed. ConclusionsAltogether, we show that despite high N deposition, this early forest succession still shows conservative N cycling characteristics, which are likely indicating N limitation early on in secondary forest succession.

When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes., Frontiers in Ecology and Evolution, 9, ISSN:2296-701X

International audience; AbstractKey messageTree heights in the central Congo Basin are overestimated using best-available height-diameter models. These errors are propagated into the estimation of aboveground biomass and canopy height, causing significant bias when used for calibration of remote sensing products in this region.ContextTree height-diameter models are important components of estimating aboveground biomass (AGB) and calibrating remote sensing products in tropical forests.AimsFor a data-poor area of the central Congo Basin, we quantified height-diameter model performance of local, regional and pan-tropical models for their use in estimating AGB and canopy height.MethodsAt three old-growth forest sites, we assessed the bias introduced in height estimation by regional and pan-tropical height-diameter models. We developed an optimal local model with site-level randomizations accounted for by using a mixed-effects modeling approach. We quantified the error propagation of modeled heights for estimating AGB and canopy height.ResultsRegional and pan-tropical height-diameter models produced a significant overestimation in tree height, propagating into significant overestimations of AGB and Lorey’s height. The pan-tropical model accounting for climatic drivers performed better than the regional models. We present a local height-diameter model which produced nonsignificant errors for AGB and canopy height estimations at our study area.ConclusionThe application of general models at our study area introduced bias in tree height estimations and the derived stand-level variables. Improved delimitation of regions in tropical Africa with similar forest structure is needed to produce models fit for calibrating remote sensing products.

International audience; Improving the precision of forest biomass estimates requires prioritizing the different sources of errors. In a tropical moist forest in central Africa, the choice of the allometric equation was found to be the main source of error.Context: When estimating the forest biomass at the landscape level using forest inventory data and allometric models, there is a chain of propagation of errors including the measurement errors, the models' prediction error, the error due to the model choice, and the sampling error.Aims: This study aims at comparing the contributions of these different sources of error to the total error, to prioritize them, and improve the precision of biomass estimates.Methods: Using a 9-ha permanent sample plot in a moist forest near Kisangani in the Democratic Republic of Congo and seven competing allometric models, we estimated the contributions of the different sources of error to the total error of the per hectare biomass estimate, for plot sizes ranging from 0.04 to 1 ha.Results: When there was no a priori on which model being the best and for 1-ha plots, the error due to the model choice was the largest source of error (76 % of the total error). Using weights to combine the predictions of the different models into a single ensemble prediction strongly reduced this error.Conclusion: Collecting training data sets on tree biomass at many sites would be needed to improve the precision of forest biomass estimates in central Africa.

La présente étude analyse la perte du couvert forestier dans la zone du projet pilote REDD+ intégré d’Isangi entre 2002 et 2010. La région est l’une des zones de la République démocratique du Congo où les ressources naturelles sont soumises à une forte pression anthropique. Cette étude a permis, grâce aux techniques de détections multi-temporelles des chan- gements combinées aux enquêtes menées sur le terrain, de cartographier les différentes classes d’occupation des sols mais aussi de déterminer les zones les plus affectées par la perte du couvert forestier. Le taux annuel de déforestation est évalué à 0,13 % (330 ha par an) et les émissions associées représentent envi- ron 196 000 tonnes de CO2 par an. Les résultats des entretiens auprès des villa- geois indiquent que les principales causes de la déforestation et de la dégra- dation des forêts sont l’agriculture itiné- rante sur brûlis et l’exploitation de bois (bois de chauffage, charbon de bois et bois de construction). Les préconisations afin de ralentir le phénomène de défores- tation dans la région sont d’améliorer la production agricole, de formaliser l’ex- ploitation de bois et de diversifier les sources d’approvisionnement des pro- duits ligneux.

The study of tree assemblages in tropical forests is gaining new impetus with the need to assess carbon emissions at high precision and resolution, while limiting the erosion of diversity and promoting sustainable forest management. The objective of this study was to (i) investigate the respective roles of topographic / soil gradients and endogenous dynamics in shaping local variations in dominance; (ii) demonstrate the feasibility of studying canopy texture by harmonizing Fourier-based Textural Ordination (FOTO) indices of two GeoEye - 50 cm images, acquired from different phenologic seasons, to calibrate AGB inversion model using inventory plots. The study was conducted in Uma forest, East of Kisangani, Democratic Republic of Congo. Dataset of 30 1-ha plots, in which all trees above 10 cm diameter at 1.30 m height (DBH) were measured and identified. Standard physical and chemical properties of soil samples were determined (macro-nutrients, textural classes and pH) and a digital elevation model (SRTM 30 m) was used to infer relevant topographical features (altitude and hydromorphy). The forest in the study area is characterized by variations in the abundance of three dominant species: Petersianthus macrocarpus (P. BEAUV.) LIBEN, Gilbertiodendron dewevrei (De Wild.) J. Léonard and Julbernardia seretii (DE WILD.) TROUPIN, one non-pioneer, light demanding species and two late successional, shade tolerant species respectively. These variations occur nearly independently of variations in the substratum or topography, despite important gradients of the range of considered variables. Analyzing differential relative abundance of the three dominant species in the lower strata and in the canopy, did not provide evidence of shifts in dominance, in which a species would obviously tend to replace another through time in any of the three floristic groups. This suggests that in this study area the states of dominance in the vegetation are stable across generations, that successional dynamics are very slow or that they are localized to peculiar locations. Using FOTO method, this study documents a strong relation between observed and predicted AGBs, without cross validation (R² of the linear regression reached 0.82 (mean square error = 27.24 T/ha). This correlation was still present, although weaker, with cross validation (R² of the linear regression between observed and predicted AGBs = 0.64). The mean square error increases to 46.68 T/ha after cross validation for a mean of 450 T/ha. This result confirms the potential of FOTO indices of optical very high resolution satellite images to quantify aboveground biomass without no signal saturation in high AGB tropical forests.; Les études des déterminants des groupements végétaux ont pris un nouvel élan avec la nécessité de quantifier avec précision les stocks de carbone à partir des données satellitaires de résolution métrique, tout en limitant l'érosion de la biodiversité et en promouvant une gestion durable des forêts. La présente étude se déroule dans la forêt de Uma, située à l’Est de Kisangani, en République Démocratique du Congo, entre les points kilométriques 65 et 90 sur la route nationale numéro 4. L’objectif de cette étude était (i) d’identifier les déterminants (sol, topographie, structure et / ou succession) des groupements végétaux qui dominent dans la forêt de Uma et (ii) estimer leurs stocks de carbone. Les données étaient collectées dans 30 parcelles de 1 ha chacune, dans lesquelles tous les arbres ≥ 10 cm de diamètre ont été mesurés et identifiés. Les échantillons de sol ont été analysés pour les variables pédologiques standard (macronutriments, classes de texture, pH) et un modèle numérique de terrain a été utilisé pour déduire les caractéristiques topographiques (altitude et hydromorphie). Deux images GeoEye – 50 cm aux géométries d’acquisition (angles soleil-capteur) très semblables, ont servi dans un modèle d’estimation de la biomasse des arbres sur 260 km² de superficie sur base de 30 parcelles de 1 ha chacune. Les résultats obtenus indiquent que, trois groupements végétaux dominent dans la forêt de Uma. Les espèces dominantes de chacun de ces groupements sont : Petersianthus macrocarpus (P. BEAUV.) LIBEN, Gilbertiodendron dewevrei (De Wild.) J. Léonard et Julbernardia seretii (DE WILD.) TROUPIN, respectivement une espèce non pionnière, exigeante en lumière et deux espèces tardives dans la succession forestière, tolérantes à l'ombrage. Ces groupements végétaux ne sont liés à aucune variable de l’environnement. La succession des espèces dominantes est au point mort. Ce résultat est évocateur soit, d'un modèle émergent, soit de multiples états stables induits par des rétroactions biologiques. La bonne relation entre la biomasse des parcelles et celle prédite sur les images de texture, a permis de produire la carte de biomasse de la forêt de Uma. A l’échelle de la parcelle, l’erreur quadratique moyenne est de 27,24 T/ha hors validation croisée (R²=0,82) et remonte à 46,68 T/ha (R²=0,61), après validation croisée pour une moyenne de 450 T/ha. Ce résultat démontre le potentiel des estimateurs de texture des images métriques dans la généralisation des biomasses de forêt sur les espaces non couverts par les données de terrain dans les forêts denses où la plupart des autres signaux de télédection saturent