Abstract: Soil samples were collected in six South American countries in a total of 71 different 1 ha forest plots across the Amazon Basin as part of the RAINFOR project. They were analysed for total and exchangeable cations, C, N, pH with various P fractions also determined. Physical properties were also examined and an index of soil physical quality proposed. A diverse range of soils was found. For the western areas near the Andean cordillera and the southern and northern fringes, soils tend to be distributed among the lower pedogenetic levels, while the central and eastern areas of Amazonia have more intensely weathered soils. This gives rise to a large variation of soil chemical and physical properties across the Basin, with soil properties varying predictably along a gradient of pedogenic development. Nutrient pools generally increased slightly in concentration from the youngest to the intermediate aged soils after which a gradual decline was observed with the lowest values found in the most weathered soils. Soil physical properties were strongly correlated with soil fertility, with favourable physical properties occurring in highly weathered and nutrient depleted soils and with the least weathered, more fertile soils having higher incidence of limiting physical properties. Soil phosphorus concentrations varied markedly in accordance with weathering extent and appear to exert an important influence on the nitrogen cycle of Amazon forest soils.

Abstract: We analysed 1040 individual trees, located in 62 plots across the Amazon Basin for leaf mass per unit area (M-A), foliar carbon isotopic composition (delta C-13) and leaf level concentrations of C, N, P, Ca, Mg, K and Al. All trees were identified to the species level with the dataset containing 58 families, 236 genera and 508 species, distributed across a wide range of soil types and precipitation regimes. Some foliar characteristics such as M-A, [C], [N] and [Mg] emerge as highly constrained by the taxonomic affiliation of tree species, but with others such as [P], [K], [Ca] and delta C-13 also strongly influenced by site growing conditions. By removing the environmental contribution to trait variation, we find that intrinsic values of most trait pairs coordinate, although different species ( characterised by different trait suites) are found at discrete locations along a common axis of coordination. Species that tend to occupy higher fertility soils are characterised by a lower M-A and have a higher intrinsic [N], [P], [K], [Mg] and delta C-13 than their lower fertility counterparts. Despite this consistency, different scaling patterns were observed between low and high fertility sites. Inter-relationships are thus substantially modified by growth environment. Analysing the environmental component of trait variation, we found soil fertility to be the most important predictor, influencing all leaf nutrient concentrations and delta C-13 and reducing M-A. Mean annual temperature was negatively associated with leaf level [N], [P] and [K] concentrations. Total annual precipitation positively influences M-A, [C] and delta C-13, but with a negative impact on [Mg]. These results provide a first basis for understanding the relationship between the physiological functioning and distribution of tree species across Amazonia.

Abstract: The fixation of carbon in tropical forests mainly occurs through the production of wood and leaves, both being the principal components of net primary production. Currently field and satellite observations are independently used to describe the forest carbon cycle, but the link between satellite-derived forest phenology and field-derived forest productivity remains opaque. We used a unique combination of a MODIS enhanced vegetation index (EVI) dataset, a wood production model based on climate data and direct litterfall observations at an intra-annual timescale in order to question the synchronism of leaf and wood production in tropical forests. Even though leaf and wood biomass fluxes had the same range (respectively 2.4 ± 1.4 and 2.2 ± 0.4 Mg C ha-1 yr-1), they occurred separately in time. EVI increased with leaf renewal at the beginning of the dry season, when solar irradiance was at its maximum. At this time, wood production stopped. At the onset of the rainy season, when new leaves were fully mature and water available again, wood production quickly increased to reach its maximum in less than a month, reflecting a change in carbon allocation from short-lived pools (leaves) to long-lived pools (wood). The time lag between peaks of EVI and wood production (109 days) revealed a substantial decoupling between the leaf renewal assumed to be driven by irradiance and the water-driven wood production. Our work is a first attempt to link EVI data, wood production and leaf phenology at a seasonal timescale in a tropical evergreen rainforest and pave the way to develop more sophisticated global carbon cycle models in tropical forests. © 2013 Author(s).

Abstract: Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial “dilution” bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.