Auteurs, date et publication :
Auteurs Julie Bossu , Romain Lehnebach , Stephane Corn , Arnaud Regazzi , Jacques Beauchêne , Bruno Clair
Publication : Trees
Date : 2018
Pages : 1–13
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Resource control over abundance, structure and functional diversity of soil microbial communities is a key determinant of soil processes and related ecosystem functioning. Copiotrophic organisms tend to be found in environments which are rich in nutrients, particularly carbon, in contrast to oligotrophs, which survive in much lower carbon concentrations. We hypothesized that microbial biomass, activity and community structure in nutrient‐poor soils of an Amazonian rain forest are limited by multiple elements in interaction. We tested this hypothesis with a fertilization experiment by adding C (as cellulose), N (as urea) and P (as phosphate) in all possible combinations to a total of 40 plots of an undisturbed tropical forest in French Guiana. After 2 years of fertilization, we measured a 47% higher biomass, a 21% increase in substrate‐induced respiration rate and a 5‐fold higher rate of decomposition of cellulose paper discs of soil microbial communities that grew in P‐fertilized plots compared to plots without P fertilization. These responses were amplified with a simultaneous C fertilization suggesting P and C colimitation of soil micro‐organisms at our study site. Moreover, P fertilization modified microbial community structure (PLFAs) to a more copiotrophic bacterial community indicated by a significant decrease in the Gram‐positive : Gram‐negative ratio. The Fungi : Bacteria ratio increased in N fertilized plots, suggesting that fungi are relatively more limited by N than bacteria. Changes in microbial community structure did not affect rates of general processes such as glucose mineralization and cellulose paper decomposition. In contrast, community level physiological profiles under P fertilization combined with either C or N fertilization or both differed strongly from all other treatments, indicating functionally different microbial communities. While P appears to be the most critical from the three major elements we manipulated, the strongest effects were observed in combination with either supplementary C or N addition in support of multiple element control on soil microbial functioning and community structure. We conclude that the soil microbial community in the studied tropical rain forest and the processes it drives is finely tuned by the relative availability in C, N and P. Any shifts in the relative abundance of these key elements may affect spatial and temporal heterogeneity in microbial community structure, their associated functions and the dynamics of C and nutrients in tropical ecosystems.
Auteurs, date et publication :
Auteurs Nicolas Fanin , Stephan Hättenschwiler , Heidy Schimann , Nathalie Fromin , Joseph K. Bailey
Publication : Functional Ecology
Date : 2015
Volume : 29
Issue : 1
Pages : 140–150
Catégorie(s)
#CIRAD #FORET ParacouAuteurs, date et publication :
Auteurs M. Fournier , J. Dlouhá , G. Jaouen , T. Almeras
Publication : Journal of Experimental Botany
Date : 2013
Volume : 64
Issue : 15
Pages : 4793–4815
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Organized forestry in Brazil began in the late 1960s, stimulated by a government policy which subsidized afforestation programs from 1967 to 1989 to develop an internationally-competitive wood-based industry, managed by the private sector. Currently, planted forests in Brazil total about 6.9 million ha, from which 4.9 million ha is planted with eucalypt (around 25% of world plantation), 1.6 million ha with pine, and 0.42 M ha with other species. Roundwood consumption of forest plantations totaled 170.1 million m3 in 2011, eucalypt plantation accounted for 80.6% of this total. Most eucalypt plantations are managed in short rotations (6-8 years) and are established in regions with water, nutritional and frost stresses of low to high degrees. The mean annual increment is 40 m3 ha?1 year?1 roundwood, ranging from 25 to 60 m3 ha?1 year?1 depending on the level of environmental stress. Improving natural resources use efficiency by breeding and matching genotypes to sites and using appropriate site management practices is a key challenge to sustain or increase productivity. The wide range of eucalypt species and hybrids with different climatic and edaphic suitability associated with the easy propagation by seeds and cloning allow the adaptation of plantations to various tropical and subtropical regions in Brazil. The possibility of using eucalypt wood in a range of purposes has led large and small enterprises to establish eucalypt forests for multiple uses. The desirable characteristics in association with the accumulated knowledge on eucalypt silviculture encourage the use of this genus in most plantations. The most important factors in the selective process for a genotype are wood characteristics, productivity level, susceptibility to pests and diseases, drought tolerance, especially in tropical regions (frost free), and frost tolerance in subtropical regions (mostly without water deficit). In regions with pronounced seasonality and moderate to long drought periods, the planting of hybrid genotypes predominates, propagated by cloning. Under subtropical conditions, the planting of single species predominates, propagated by seed. Clonal plantations with interspecific hybrids have been fundamental for eucalypt adaptation in regions under water and nutritional stresses. Given the rapid advances in eucalypt breeding, regarding adaptation to water stress and resistance to diseases and pests, and the adoption of clonal propagation techniques, genotypes are rapidly becoming obsolete and are replaced by more productive ones after harvesting. Thus, the replanting of crops has become a common procedure after the second half of the 1990s in Brazil. This paper describes the basic requirements for integrating genetic and silvicultural strategies to minimize abiotic and biotic constraints in eucalypt plantations. (Résumé d'auteur)
Auteurs, date et publication :
Auteurs Jose Leonardo De Moraes Gonçalves , Clayton Alcarde Alvares , Antonio Rioyei Higa , James Stahl , Silvio Frosini De Barros Ferraz , Walter De Paula Lima , Pedro Henrique Santin Brancalion , Ayeska Hubner , Jean-Pierre Bouillet , Jean-Paul Laclau , Daniel Epron , Yann Nouvellon
Publication : Forest Ecology and Management
Date : 2025
Volume : 301
Pages : 6-27
Catégorie(s)
#CIRAD #FORET Itatinga #INRAERésumé
The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth's radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.
Auteurs, date et publication :
Auteurs Francis M Lopes , Emanuel Dutra , Isabel F. Trigo
Publication : Remote Sensing
Date : 2025
Volume : 14
Issue : 7
Catégorie(s)
#CIRAD #FORET ParacouRésumé
Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.
Auteurs, date et publication :
Auteurs Lidong Mo , Constantin M. Zohner , Peter B. Reich , Jingjing Liang , Sergio de Miguel , Gert-Jan Nabuurs , Susanne S. Renner , Johan van den Hoogen , Arnan Araza , Martin Herold , Leila Mirzagholi , Haozhi Ma , Colin Averill , Oliver L. Phillips , Javier G. P. Gamarra , Iris Hordijk , Devin Routh , Meinrad Abegg , Yves C. Adou Yao , Giorgio Alberti
Publication : Nature
Date : 2025
Catégorie(s)
#CIRAD #FORET ParacouRésumé
LiDAR technology has been widely used to characterize structural parameters of forest ecosystems, which in turn are valuable information for forest monitoring. GEDI is a spaceborne LiDAR system specifically designed to measure vegetation’s vertical structure, and it has been acquiring waveforms on a global scale since April 2019. In particular, canopy height is an important descriptor of forest ecosystems, as it allows for quantifying biomass and other inventory information. This paper analyzes the accuracy of canopy height estimates from GEDI data over tropical forests in French Guiana and Gabon. The influence of various signal acquisition and processing parameters is assessed to highlight how they impact the estimation of canopy heights. Canopy height models derived from airborne LiDAR data are used as reference heights. Several linear and non-linear approaches are tested given the richness of the available GEDI information. The results show that the use of regression models built on multiple GEDI metrics allows for reaching improved accuracies compared to a direct estimation from a single GEDI height metric. In a notable way, random forest improves the canopy height estimation accuracy by almost 80% (in terms of RMSE) compared to the use of rh_95 as a direct proxy of canopy height. Additionally, convolutional neural networks calibrated on GEDI waveforms exhibit similar results to the ones of other regression models. Beam type as well as beam sensitivity, which are related to laser penetration, appear as parameters of major influence on the data derived from GEDI waveforms and used as input for canopy height estimation. Therefore, we recommend the use of only power and high-sensitivity beams when sufficient data are available. Finally, we note that regression models trained on reference data can be transferred across study sites that share identical environmental conditions.
Auteurs, date et publication :
Auteurs Kamel Lahssini , Nicolas Baghdadi , Guerric le Maire , Ibrahim Fayad
Publication : Remote Sensing
Date : 2022
Volume : 14
Issue : 24
Pages : 6264
Catégorie(s)
#CIRAD #CNRS #FORET Nouragues #FORET ParacouRésumé
Recent studies showed a positive tree response to Na addition in K-depleted tropical soils. Our study aimed to gain insight into the effects of K and Na fertilizations on leaf area components for a widely planted tree species.
Auteurs, date et publication :
Auteurs P. Battie-Laclau , M.-C. Piccolo , Bruna C. Arenque , C. Beri , L. Mietton , M.-R.-A. Muniz , L. Jordan-Meille , M.-S. Buckeridge , Y. Nouvellon , J. Ranger , J.-P. Bouillet
Publication : Plant and Soil
Date : 2025
Volume : 371
Issue : 1
Pages : 19-35
Catégorie(s)
#CIRAD #FORET Itatinga #INRAEAuteurs, date et publication :
Auteurs Clément Stahl , Benoit Burban , Fabien Wagner , Jean-Yves Goret , Félix Bompy , Damien Bonal
Publication : Biotropica
Date : 2013
Volume : 45
Issue : 2
Pages : 155–164
Catégorie(s)
#CIRAD #FORET ParacouRésumé
While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long-lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought-induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model-data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post-drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust-boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought-free conditions. Overall, allocation shifts accounted for 65% [45%–75%] of drought legacy effects in modeled NBE. In summary, drought-induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land-atmosphere CO2 exchanges and should be accounted for in ecosystem models.
Auteurs, date et publication :
Auteurs Matthew A. Worden , Caroline A. Famiglietti , Paul A. Levine , Shuang Ma , A. Anthony Bloom , Damien Bonal , Clément Stahl , Alexandra G. Konings
Publication : Global Change Biology
Date : 2025
Volume : 30
Issue : 5
Pages : e17287