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Genève, Switzerland

Rutishauser E.,Carboforexpert | Rutishauser E.,CIRAD - Agricultural Research for Development | Herault B.,UMR EcoFoG | Baraloto C.,French National Institute for Agricultural Research | And 23 more authors.
Current Biology | Year: 2015

Summary While around 20% of the Amazonian forest has been cleared for pastures and agriculture, one fourth of the remaining forest is dedicated to wood production [1]. Most of these production forests have been or will be selectively harvested for commercial timber, but recent studies show that even soon after logging, harvested stands retain much of their tree-biomass carbon and biodiversity [2,3]. Comparing species richness of various animal taxa among logged and unlogged forests across the tropics, Burivalova et al.[4] found that despite some variability among taxa, biodiversity loss was generally explained by logging intensity (the number of trees extracted). Here, we use a network of 79 permanent sample plots (376 ha total) located at 10 sites across the Amazon Basin [5] to assess the main drivers of time-to-recovery of post-logging tree carbon (Table S1). Recovery time is of direct relevance to policies governing management practices (i.e., allowable volumes cut and cutting cycle lengths), and indirectly to forest-based climate change mitigation interventions. © 2015 Elsevier Ltd. Source


Piponiot C.,University of the French West Indies and Guiana | Cabon A.,Center Tecnologic Forestal Of Catalonia | Descroix L.,ONF Guyane | Dourdain A.,University of the French West Indies and Guiana | And 5 more authors.
Carbon Balance and Management | Year: 2016

Background: Managed forests are a major component of tropical landscapes. Production forests as designated by national forest services cover up to 400 million ha, i.e. half of the forested area in the humid tropics. Forest management thus plays a major role in the global carbon budget, but with a lack of unified method to estimate carbon fluxes from tropical managed forests. In this study we propose a new time- and spatially-explicit methodology to estimate the above-ground carbon budget of selective logging at regional scale. Results: The yearly balance of a logging unit, i.e. the elementary management unit of a forest estate, is modelled by aggregating three sub-models encompassing (i) emissions from extracted wood, (ii) emissions from logging damage and deforested areas and (iii) carbon storage from post-logging recovery. Models are parametrised and uncertainties are propagated through a MCMC algorithm. As a case study, we used 38 years of National Forest Inventories in French Guiana, northeastern Amazonia, to estimate the above-ground carbon balance (i.e. the net carbon exchange with the atmosphere) of selectively logged forests. Over this period, the net carbon balance of selective logging in the French Guianan Permanent Forest Estate is estimated to be comprised between 0.12 and 1.33 Tg C, with a median value of 0.64 Tg C. Uncertainties over the model could be diminished by improving the accuracy of both logging damage and large woody necromass decay submodels. Conclusions: We propose an innovating carbon accounting framework relying upon basic logging statistics. This flexible tool allows carbon budget of tropical managed forests to be estimated in a wide range of tropical regions. © 2016 The Author(s). Source


Sist P.,CIRAD - Agricultural Research for Development | Mazzei L.,Embrapa Amazonia Oriental | Blanc L.,CIRAD - Agricultural Research for Development | Rutishauser E.,Carboforexpert
Forest Ecology and Management | Year: 2014

The long term effect of Reduced-Impact Logging (RIL) on above-ground live biomass (AGB) dynamics was investigated in 18 1-ha logged over permanent sample plots set up in a terra firme rain forest in the Eastern Amazon (Brazil, Paragominas). Both tree survival and growth were investigated among three Diameter at Breath Height (DBH) classes (20-40, 40-60, ≥60cm) to assess the contribution of each DBH class to the post-logging AGB recovery. Before logging, mean tree density and AGB per plot (dbh≥20cm) were 187±14 trees ha-1 and 377.6±62.8Mgha-1 respectively. Although big trees (dbh≥60cm) only represented 9.3% of the total tree density, they gathered almost half of total AGB. During the post-logging period (8years), the mortality of large trees was found to drive the annual net changes and largely overcame the AGB gain in the smaller DBH classes. Indeed, plots with high post-logging mortality of large trees showed negative carbon balance t over the study period (8years). The over mortality of large trees injured by logging contributed significantly to the annual AGB losses (up to 40%) in the first years after logging. Due to the overwhelming importance of this size class in carbon stocks and dynamic, reducing logging damages and intensity might have great impact in the post-logging biomass dynamics. We estimated that reducing logging intensity from 6 to 3 stems ha-1 would save 27.7Mg C ha-1 for a 35years rotation cycle. To compensate this loss of profits, compensatory payments of avoided CO2 emission should worth US $ 6.5/Mg of CO2. This price falls into the range of prices of the international carbon market. Sustainable forest management aiming at enhancing carbon stocks could therefore promote the preservation of the large trees. At our study site, we recommend the adoption of a maximum diameter cutting limit of 110cm. © 2014 Elsevier B.V. Source


Picard N.,British Petroleum | Picard N.,CIRAD - Agricultural Research for Development | Rutishauser E.,Carboforexpert | Ploton P.,IRD Montpellier | And 2 more authors.
Forest Ecology and Management | Year: 2015

The increasing number of model types that are used to predict tree biomass from diameter, height and wood density has brought questioning about the biological relevance of complex allometries (i.e. non-power models). Statistical issues such as collinearity among predictors and unreliable coefficient estimates have also been associated with complex allometric models. Using a data set of 225 trees from central Africa, we assessed the relevance of simple allometry (i.e. power model) versus complex allometry to predict tree biomass. A complex allometric model of biomass was developed based on a model of resource partition between dbh and height growths. Although being a good model for biomass prediction, the power model was outperformed by the complex allometric model. A careful examination showed that the power model could be segmented into two pieces of power models. Using tree diameter and height as separated predictors improved the biomass prediction, irrespective of the collinearity between these two predictors. A critical value of 25% for the PRSE statistic used to assess the reliability of coefficient estimates corresponded to a significance level of 10-5-10-4 and was thus unreasonably low. We conclude that growth theories should be developed to explain allometric models, but that the arbitration between these models should ultimately rely on observed data, not on theories. © 2015 Elsevier B.V. Source


Theilade I.,Copenhagen University | Rutishauser E.,Carboforexpert | Poulsen M.K.,Nordic Agency for Development and Ecology NORDECO
Carbon Balance and Management | Year: 2015

Background: REDD+ programs rely on accurate forest carbon monitoring. Several REDD+ projects have recently shown that local communities can monitor above ground biomass as well as external professionals, but at lower costs. However, the precision and accuracy of carbon monitoring conducted by local communities have rarely been assessed in the tropics. The aim of this study was to investigate different sources of error in tree biomass measurements conducted by community monitors and determine the effect on biomass estimates. Furthermore, we explored the potential of local ecological knowledge to assess wood density and botanical identification of trees. Results: Community monitors were able to measure tree DBH accurately, but some large errors were found in girth measurements of large and odd-shaped trees. Monitors with experience from the logging industry performed better than monitors without previous experience. Indeed, only experienced monitors were able to discriminate trees with low wood densities. Local ecological knowledge did not allow consistent tree identification across monitors. Conclusion: Future REDD+ programmes may benefit from the systematic training of local monitors in tree DBH measurement, with special attention given to large and odd-shaped trees. A better understanding of traditional classification systems and concepts is required for local tree identifications and wood density estimates to become useful in monitoring of biomass and tree diversity. Source

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