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Recently, several remote sensing methods have been developed to quantify the degradation of tropical forests. However, it still lacks finest spatial and temporal analysis to define trajectories of forest degradation i.e. a temporal analysis of the impacts on forest integrity. This communication aims to explore this issue and proposes a set of operational indicators to monitor forest degradation, which can constitutes a decision tool to support forestry managers and policy makers. We studied the trajectories of forest degradation in the municipality of Paragominas – PA in the eastern Brazilian Amazon between 1995 and 2009, with a focus on the forestry company Cikel (400 000 ha certified by FSC since 2001). First, we developed a semi-automatic remote sensing methodology to detect forest degradation using multi-temporal Landsat images (spatial resolution of 30m) covering the 1995-2009 period. This method included two steps: 1) Identification of logging tracks and log landings using an algorithm of Bourbier et al. (2013). This algorithm uses spectral indices and morphological filters to strengthen the spectral contrasts between bare soil and forest cover. 2) Identification of logging gaps - which are characterised by senescent vegetation due to trees fall - using a Spectral Mixture Analysis carried out in CLASlite (Asner et al., 2009) and a fraction index (Souza et al., 2013). So, we obtained annual maps identifying these three major impacts. Secondly, we calculated annual landscape metrics of forest degradation using the R package "SpatialEco". Then, we calculated indicators which synthetize information about logging impacts and logging frequencies over the period from these annual degradation metrics. Finally, we selected a set of 6 indicators and statistically analysed the trajectories of degradation occurring in Paragominas using ACP and CAH. Our results emphasize four major degradation trajectories from well managed forests to highly-logged forests. They clearly show a difference between legal and illegal logging in terms of forest degradation. Moreover, they indicate that impacts of FSC certification on forest degradation was positive. Degradation was statistically lower in the certified logged plots compared to the uncertified plots. These set of indicators are adequate to monitor forest degradation through space and provide guidance to policy-makers for a better management of forest resources. (Texte intégral)

Background. Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. Method. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 123 tree genera, in 140,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Result. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area at the present time. At the community level, the floristic and functional composition of tree genera is also inferred with a good accuracy. Finally, using spatially explicit null models, we show that species-climate association are in most cases not better than chance, thus challenging our ability to predict how forest composition will be affected by climatic changes. Conclusion. Overall, our study shows that tropical tree distributions can be predicted with good accuracy at the present time, offering new perspectives to manage tropical forests at large spatial scales, but that predicting shifts in species distribution under climate change scenarios is challenging. (Texte intégral)

Background: Large-scale ecosystem services (ES) mapping is a challenge for environmental management. A possible and low-cost method is the up-scaling approach, which we tested on Pará State, Brazil. Method: We mapped four ES indicators (vegetation carbon stocks, rates of water infiltration into soil and a biodiversity and a chemical quality index) at the regional scale, from field and remote sensing data. To do so, we first classified the land cover from MODIS images. Then, we averaged the ES indicators (field data) per MODIS land cover class. Finally, we evaluated regional maps' accuracy through three different procedures: first, we mapped the variability of the ES indicators per land cover type. Secondly, we compared, statistically and visually, the regional ES indicators maps with local scale maps made from statistical models that linked remote sensing (Landsat and DEM Aster) and field (ES indicators) data. Finally, we calculated the correlations between our predicted values and independent datasets. Results: Our results showed the spatial distribution of some ES indicators for Pará Sate. ES indicators providing is the highest in the forest, except the soil chemical quality index. Yet, Pará State, still mainly covered by forests, has known a large movement of deforestation from the east to the west, despite the creation of protected areas. The western part, marginally affected by deforestation, is thus characterized by high ES providing. On the contrary, the eastern part, severely damaged by deforestation, is associated with poor ES providing. Our results also showed the unequal capacity to get reliable ES maps at the regional scale. In other words, our results show the additional complexity of modeling ES whose variations are very partially based on land cover changes, such as the biodiversity index. Discussion: Up-scaling approaches should bridge the gap between the spatial scales. These approaches complete large-scale maps by local knowledge and enable the estimation of the uncertainty of the maps and ES spatial representation at different spatial scales. They then may help to justify conservation actions by targeting the areas greatly damaged. Regional maps for other indicators of ES at the regional scale should be investigated. From this study, one could also decide to implement the proposed methodology to map ES indicators at a larger scale, even up to the global scale, if one possesses sampling data all over the world. (Texte intégral)

Background: Understanding the reaction of ecosystems to climate change and anthropogenic pressure is a central question in ecology and environmental sciences. In the terrestrial tropics, theoretical and empirical works suggest that once external disturbances have reached a given threshold, forest-savanna systems can switch from one state to another. Considering the multiplicity of the tropical forest systems, we make the assumption that numerous shifts may actually occur within the forest itself, without changes in forest cover but with risks of critical modifications in forest functioning. Methods: To test this hypothesis, we used a finite mixture of regression models aiming at simultaneously predicting and grouping forest functional profiles at the stand level with respect to anthropogenic pressure, climate and soil. The model is built on a dataset of more than 140 000 plots of 0.5-ha each gathered from Central African forest companies. Forest stand functions are analyzed through two key functional traits: the successional status - pioneer vs. non-pioneer trees- and the leaf phenology - evergreen vs. deciduous trees. Results: Our model captured a significant part of variation in the functional composition over the study area and revealed how anthropogenic pressure, climate change, soils or their combination lead to profound modifications within the forests. In particular, we showed that shifts from evergreen to deciduous stands can be mediated both by anthropogenic pressure or climate change. Discussion: This work shows for the first time how external forcing may jointly lead to multiple shifts in the functional composition of tropical forests. Our model allowed to predict directional changes in forest functioning according to anthropogenic pressure and climate thus opening new perspectives in theoretical ecology, global vegetation modelling and in the understanding of the vulnerability of tropical forests to global changes. (Texte intégral)