BackgroundA large proportion of the tropical rain forests of central Africa undergo periodic selective logging for timber harvesting. The REDD+ mechanism could promote less intensive logging if revenue from the additional carbon stored in the forest compensates financially for the reduced timber yield.ResultsCarbon stocks, and timber yields, and their associated values, were predicted at the scale of a forest concession in Gabon over a project scenario of 40 yr with reduced logging intensity. Considering that the timber contribution margin (i.e. the selling price of timber minus its production costs) varies between 10 and US$40 m −3, the minimum price of carbon that enables carbon revenues to compensate forgone timber benefits ranges between US$4.4 and US$25.9/tCO 2 depending on the management scenario implemented.ConclusionsWhere multiple suppliers of emission reductions compete in a REDD+ carbon market, tropical timber companies are likely to change their management practices only if very favourable conditions are met, namely if the timber contribution margin remains low enough and if alternative management practices and associated incentives are appropriately chosen.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-014-0004-3) contains supplementary material, which is available to authorized users.

No abstract

Impacts of climate change on the future dynamics of Central African forests are still largely unknown, despite the acuteness of the expected climate changes and the extent of these forests. The high diversity of species and the potentially equivalent diversity of responses to climate modifications are major difficulties encountered when using predictive models to evaluate these impacts. In this study, we applied a mixture of inhomogeneous matrix models to a long-term experimental site located in M'Baïki forests, in the Central African Republic. This model allows the clustering of tree species into processesbased groups while simultaneously selecting explanatory climate and stand variables at the group-level. Using downscaled outputs of 10general circulation models (GCM), we projected the future forest dynamics up to the end of the century, under constant climate and Representative Concentration Pathways4.5 and8.5. Through comparative analyses across GCM versions, we identified tree species meta-groups, which are more adapted than ecological guilds to describe the diversity of tree species dynamics and their responses to climate change. Projections under constant climate were consistent with a forest ageing phenomenon, with a slowdown in tree growth and a reduction of the relative abundance of short-lived pioneers. Projections under climate change showed a general increase in growth, mortality and recruitment. This acceleration in forest dynamics led to a strong natural thinning effect, with different magnitudes across species. These differences caused a compositional shift in favour of long-lived pioneers, at the detriment of shade-bearers. Consistent with other field studies and projections, our results show the importance of elucidating the diversity of tree species responses when considering the general sensitivity of Central African forests dynamics to climate change.

Understanding how environmental factors could impact population dynamics is of primary importance for species conservation. Matrix population models are widely used to predict population dynamics. However, in species-rich ecosystems with many rare species, the small population sizes hinder a good fit of species-specific models. In addition, classical matrix models do not take into account environmental variability. We propose a mixture of regression models with variable selection allowing the simultaneous clustering of species into groups according to vital rate information (recruitment, growth and mortality) and the identification of group-specific explicative environmental variables. We develop an inference method coupling the R packages flexmix and glmnet. We first highlight the effectiveness of the method on simulated datasets. Next, we apply it to data from a tropical rain forest in the Central African Republic. We demonstrate the accuracy of the inhomogeneous mixture matrix model in successfully reproducing stand dynamics and classifying tree species into well-differentiated groups with clear ecological interpretations. (Résumé d'auteur

No abstract

In the Congo Basin where nearly 20 million ha of concessions are exploited according to management plans, improved forest management (IFM) has become a strategy of prime importance when setting up the REDD+ mechanism. For logging companies, REDD+ projects provide the opportunity to compensate a voluntary reduction of the logging intensity by valuing the associated carbon gain. We explored, from the perspective of a logging company, a range of scenarios for reducing logging intensity so as to assess the possibilities for emissions reductions and to evaluate the financial feasibility of such projects. On the basis of Monte Carlo simulations for a typical export-oriented forest concession, we calculated intervals of break-even prices of permanent carbon credits. We show that logging intensity reduction is an attractive option when there is a complete cessation of logging, and for little exploited and low-profit forests. The most feasible IFM projects would be those that require a major reduction of logging intensity. Our work suggests that—instead of improving forest logging techniques—IFM projects based on a voluntary reduction of logging intensity would rather lead the exclusive choice of carbon or timber valuation. Carbon market prices are too low to be an incentive to change logging practices toward more climate-smart forest management, and a change of paradigm to change actors' behaviors would be needed. (Résumé d'auteur

No abstract