Abstract: The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000ĝ€-mmĝ€-yrĝ'1 (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000ĝ€-mmĝ€-yrĝ'1. Author(s) 2016.

Abstract: The high species diversity of some ecosystems like tropical rainforests goes in pair with the scarcity of data for most species. This hinders the development of models that require enough data for fitting. The solution commonly adopted by modellers consists in grouping species to form more sizeable data sets. Classical methods for grouping species such as hierarchical cluster analysis do not take account of the variability of the species characteristics used for clustering. In this study a clustering method based on aggregation theory is presented. It takes account of the variability of species characteristics by searching for the grouping that minimizes the quadratic error (square bias plus variance) of some model's prediction. This method allows one to check whether the gain in variance brought by data pooling compensate for the bias that it introduces. This method was applied to a data set on 94 tree species in a tropical rainforest in French Guiana, using a Usher matrix model to predict species dynamics. An optimal trade-off between bias and variance was found when grouping species. Grouping species appeared to decrease the quadratic error, except when the number of groups was very small. This clustering method yielded species groups similar to those of the hierarchical cluster analysis using Ward's method when variance was small, that is when the number of groups was small. (C) 2009 Elsevier B.V. All rights reserved.

Abstract: Accurate CO2 concentration gradient measurements are needed for the computation of advective flux terms, which are part of the full Net Ecosystem Exchange (NEE) budget equation. A typical draw back of current gradient measurement designs in advection research is the inadequate sampling of complex flow phenomena using too few observation points in space and time. To overcome this draw back, a new measurement design is presented which allows the parallel measurement of several sampling points at a high frequency. Due to the multi-analyzer nature of the design, inter-instrument bias becomes more of a concern compared to conventional setups. Therefore a statistical approach is presented which allows for accurate observations of concentration gradients, which are typically small in relation to analyzer accuracy, to be obtained. This bias correction approach applies a conditional, time dependent signal correction. The correction depends on a mixing index based on cross correlation analysis, which characterizes the degree of mixing of the atmosphere between individual sample points. The approach assumes statistical properties of probability density functions (pdf) of concentration differences between a sample point and the field average which are common to the pdf's from several sample points. The applicability of the assumptions made was tested by Large Eddy Simulation (LES) using the model PALM and could be verified for a test case of well mixed conditions. The study presents concentration time series before and after correction, measured at a 2 m height in the sub-canopy at the FLUXNET spruce forest site Waldstein-Weidenbrunnen (DE-Bay), analyzes the dependence of statistical parameters of pdf's from atmospheric parameters such as stratification, quantifies the errors and evaluates the performance of the bias correction approach. The improvements that are achieved by applying the bias correction approach are one order of magnitude larger than possible errors associated with it, which is a strong incentive to use the correction approach. In conclusion, the presented bias correction approach is well suited for – but not limited to – horizontal gradient measurements in a multi-analyzer setup, which would not have been reliable without this approach. Finally, possible future improvements of the bias correction approach are outlined and further fields of application indicated.

Abstract: Eperua grandiflora, which is widely distributed in the French Guiana forest region, shows high variability in decay resistance. Further information concerning this wood quality parameter is necessary, but standard testing methods are complex and time-consuming. We assessed the use of colorimetry to determine durability in heartwood samples from a range of trees. Eperua grandiflora colour parameters were measured using a CIELAB system, revealing that the tree effect was greater than the radial position and height effects. The wood samples were exposed to Coriolus versicolor and Antrodia sp. according to two European standards (En 350-1 and XP CEN TS 15083-1). Eperua grandiflora is more susceptible to brown rot. These two standards did not give the same durability classes. The high variation in natural durability was due to the tree effect. These two properties were found to be correlated and the assessment also distinguished the extreme durability classes but they are not sufficient to classify the class of durability of this species.

Abstract: Biological nitrogen fixation (BNF) is a fundamental part of nitrogen cycling in tropical forests, yet little is known about the contribution made by free-living nitrogen fixers inhabiting the often-extensive forest canopy. We used the acetylene reduction assay, calibrated with 15N2, to measure free-living BNF on forest canopy leaves, vascular epiphytes, bryophytes and canopy soil, as well as on the forest floor in leaf litter and soil. We used a combination of calculated and published component densities to upscale free-living BNF rates to the forest level. We found that bryophytes and leaves situated in the canopy in particular displayed high mass-based rates of free-living BNF. Additionally, we calculated that nearly 2 kg of nitrogen enters the forest ecosystem through free-living BNF every year, 40% of which was fixed by the various canopy components. Our results reveal that in the studied tropical lowland forest a large part of the nitrogen input through free-living BNF stems from the canopy, but also that the total nitrogen inputs by free-living BNF are lower than previously thought and comparable to the inputs of reactive nitrogen by atmospheric deposition.