Abstract: Tropical vegetation is a major source of global land surface evapotranspiration, and can thus play a major role in global hydrological cycles and global atmospheric circulation. Accurate prediction of tropical evapotranspiration is critical to our understanding of these processes under changing climate. We examined the controls on evapotranspiration in tropical vegetation at 21 pan-tropical eddy covariance sites, conducted a comprehensive and systematic evaluation of 13 evapotranspiration models at these sites, and assessed the ability to scale up model estimates of evapotranspiration for the test region of Amazonia. Net radiation was the strongest determinant of evapotranspiration (mean evaporative fraction was 0.72) and explained 87% of the variance in monthly evapotranspiration across the sites. Vapor pressure deficit was the strongest residual predictor (14%), followed by normalized difference vegetation index (9%), precipitation (6%) and wind speed (4%). The radiation-based evapotranspiration models performed best overall for three reasons: (1) the vegetation was largely decoupled from atmospheric turbulent transfer (calculated from X decoupling factor), especially at the wetter sites; (2) the resistance-based models were hindered by difficulty in consistently characterizing canopy (and stomatal) resistance in the highly diverse vegetation; (3) the temperature-based models inadequately captured the variability in tropical evapotranspiration. We evaluated the potential to predict regional evapotranspiration for one test region: Amazonia. We estimated an Amazonia-wide evapotranspiration of 1370 mm yr(-1), but this value is dependent on assumptions about energy balance closure for the tropical eddy covariance sites; a lower value (1096 mm yr(-1)) is considered in discussion on the use of flux data to validate and interpolate models.

Abstract: Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison. We developed a Zero-Inflated Poisson (ZIP) model with a latent correlated spatial structure and compared it with non-spatial ZIP and Poisson models that were either autocorrelated (Spatial Generalized Linear Mixed, SGLM) or not (generalized linear models, GLM). In our spatial models, local density autocorrelation was modeled by a Conditional Auto-Regressive (CAR) process. 13 explicative variables described ecological conditions with respect to topography, disturbance, stand structure and intraspecific processes. Models were applied to six tropical tree species with differing biological attributes: Oxandra asbeckii, Eperua falcata, Eperua grandiflora, Dicorynia guianensis, Qualea rosea, and Tachigali melinonii. We built species-specific models using a simple method of variable selection based on a latent binary indicator. Our spatial models showed a close correlation between observed and estimated densities with site spatial structure being correctly reproduced. By contrast, the non-spatial models showed poor fits. Variable selection highlighted species-specific requirements and susceptibility to local conditions. Model comparison overall showed that the SGLM was the most accurate explanatory and predictive model. Surprisingly, zero-inflated models performed less well. Although the SZIP model was relevant with respect to data distribution, and more flexible with respect to response curves, its model complexity caused marked variability in parameter estimates. In the SUM, the spatial process alone accounted for zero-inflation in the data. A refinement of the hypotheses employed at the process level could compensate for distribution flaws at the data level. This study emphasized the importance of the HBM framework in improving the modelling of density-environment relationships. (C) 2008 Elsevier B.V. All rights reserved.

Abstract: 1. Trade-offs among functional traits reveal major plant strategies that can give insight into species distributions and ecosystem processes. However, current identification of plant strategies lacks the integration of root structural traits together with leaf and stem traits. 2. We examined correlations among 14 traits representing leaf, stem and woody root tissues. Traits were measured on 1084 individuals representing 758 Neotropical tree species, across 13 sites representative of the environmental variation encompassed by three widespread habitats (seasonally flooded, clay terra firme and white-sand forests) at opposite ends of Amazonia (French Guiana and Peru). 3. Woody root traits were closely aligned with stem traits, but not with leaf traits. Altogether leaf, stem and woody root traits delineated two orthogonal axes of functional trade-offs: a first axis defined by leaf traits, corresponding to a 'leaf economics spectrum', and a second axis defined by covarying stem and woody root traits, corresponding to a 'wood economics spectrum'. These axes remained consistent when accounting for species evolutionary history with phylogenetically independent contrasts. 4. Despite the strong species turnover across sites, the covariation among root and stem structural traits as well as their orthogonality to leaf traits were strongly consistent across habitats and regions. 5. We conclude that root structural traits mirrored stem traits rather than leaf traits in Neotropical trees. Leaf and wood traits define an integrated whole-plant strategy in lowland South American forests that may contribute to a more complete understanding of plant responses to global changes in both correlative and modelling approaches. We suggest further meta-analyses in expanded environmental and geographic zones to determine the generality of this pattern. © 2012 The Authors. Functional Ecology © 2012 British Ecological Society.

Abstract: The consequences of biodiversity loss for ecosystem services largely depend on the functional identities of extirpated species. However, poor descriptions of spatial patterns of community functional composition across landscapes hamper accurate predictions, particularly in highly diverse tropical regions. Therefore, understanding how community functional composition varies across environmental gradients remains an important challenge. We sampled 15 functional traits in 800 Neotropical tree species across 13 forest plots representative of the broad climatic and soil gradients encompassed by three widespread lowland forest habitats (terra firme forests on clay-rich soils, seasonally flooded forests and white-sand forests) at opposite ends of Amazonia (Peru and French Guiana). We combined univariate and multivariate approaches to test the magnitude and predictability of environmental filtering on community leaf and wood functional composition. Directional shifts in community functional composition correlated with environmental changes across the 13 plots, with denser leaves, stems and roots in forests occurring in environments with limited water and soil-nutrient availability. Critically, these relationships allowed us to accurately predict the functional composition of 61 additional forest plots from environmental data alone. Synthesis. Environmental filtering consistently shapes the functional composition of highly diverse tropical forests at large scales across the terra firme, seasonally flooded and white-sand forests of lowland Amazonia. Environmental factors drive and allow the prediction of variation in community functional composition among habitat types in Amazonian forests. © 2013 British Ecological Society.