Terrestrial ecosystems sequester 2.1 Pg of atmospheric carbon annually. A large amount of the terrestrial sink is realized by forests. However, considerable uncertainties remain regarding the fate of this carbon over both short and long timescales. Relevant data to address these uncertainties are being collected at many sites around the world, but syntheses of these data are still sparse. To facilitate future synthesis activities, we have assembled a comprehensive global database for forest ecosystems, which includes carbon budget variables (fluxes and stocks), ecosystem traits (e.g. leaf area index, age), as well as ancillary site information such as management regime, climate, and soil characteristics. This publicly available database can be used to quantify global, regional or biome-specific carbon budgets; to re-examine established relationships; to test emerging hypotheses about ecosystem functioning [e.g. a constant net ecosystem production (NEP) to gross primary production (GPP) ratio]; and as benchmarks for model evaluations. In this paper, we present the first analysis of this database. We discuss the climatic influences on GPP, net primary production (NPP) and NEP and present the CO 2 balances for boreal, temperate, and tropical forest biomes based on micrometeorological, ecophysiological, and biometric flux and inventory estimates. Globally, GPP of forests benefited from higher temperatures and precipitation whereas NPP saturated above either a threshold of 1500 mm precipitation or a mean annual temperature of 10 1C. The global pattern in NEP was insensitive to climate and is hypothesized to be mainly determined by nonclimatic conditions such as successional stage, management, site history, and site disturbance. In all biomes, closing the CO 2 balance required the introduction of substantial biome-specific closure terms. Nonclosure was taken as an indication that respiratory processes, advection, and non-CO 2 carbon fluxes are not presently being adequately accounted for. Nomenclauture:DOC 5 dissolved organic carbon; fNPP 5 foliage component of NPP; GPP 5 gross primary production (GPP40 denotes photosynthetic uptake); mNPP 5 missing component of NPP;NBP 5 net biome production (NBP40 denotes biome uptake); NECB 5 net ecosystem carbon balance (NECB40 denotes ecosystem uptake); NEE 5 net ecosystem exchange (NEE40 denotes ecosystem uptake); NEP 5 net ecosystem production (NEP40 denotes ecosystem uptake); NPP 5 net primary production (NPP40 denotes ecosystem uptake); R a 5 autotrophic respiration (R a 40 denotes respiratory losses); R e 5 ecosystem respiration (R e 40 denotes respiratory losses); R h 5 heterotrophic respiration (R h 40 denotes respiratory losses); rNPP 5 root component of NPP;R s 5 soil respiration (R s 40 denotes respiratory losses); VOC 5 volatile organic compounds; wNPP 5 wood component of NPP

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects.We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives. Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY database (TRY-not an acronym, rather a statement of sentiment; https ://www.try-db.org; Kattge et al., 2011) was proposed with the explicit assignment to improve the availability and accessibility of plant trait data for ecology and earth system sciences. The Max Planck Institute for Biogeochemistry (MPI-BGC) offered to host the database and the different groups joined forces for this community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant ecology submitting large databases and the long-term funding by the Max Planck Society, the MPI-BGC and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which has enabled the continuous development of the TRY database.

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Interactive effects of multiple global change factors on ecosystem processes are complex. It is relatively expensive to explore those interactions in manipulative experiments. We conducted a modeling analysis to identify potentially important interactions and to stimulate hypothesis formulation for experimental research. Four models were used to quantify interactive effects of climate warming (T), altered precipitation amounts [doubled (DP) and halved (HP)] and seasonality (SP, moving precipitation in July and August to January and February to create summer drought), and elevated [CO 2 ] (C) on net primary production (NPP), heterotrophic respiration (R h ), net ecosystem production (NEP), transpiration, and runoff. We examined those responses in seven ecosystems, including forests, grasslands, and heathlands in different climate zones. The modeling analysis showed that none of the threeway interactions among T, C, and altered precipitation was substantial for either carbon or water processes, nor consistent among the seven ecosystems. However, two-way interactive effects on NPP, R h , and NEP were generally positive (i.e. amplification of one factor's effect by the other factor) between T and C or between T and DP. A negative interaction (i.e. depression of one factor's effect by the other factor) occurred for simulated NPP between T and HP. The interactive effects on runoff were positive between T and HP. Four pairs of two-way interactive effects on plant transpiration were positive and two pairs negative. In addition, wet sites generally had smaller relative changes in NPP, R h , runoff, and transpiration but larger absolute changes in NEP than dry sites in response to the treatments. The modeling results suggest new hypotheses to be tested in multifactor global change experiments. Likewise, more experimental evidence is needed for the further improvement of ecosystem models in order to adequately simulate complex interactive processes.

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The increasing number of sensor types for terrestrial remote sensing has necessitated supplementary efforts to evaluate and standardize data from the different available sensors. In this study, we assess the potential use of IKONOS, ETM+, and SPOT HRVIR sensors for leaf area index (LAI) estimation in forest stands. In situ measurements of LAI in 28 coniferous and deciduous stands are compared to reflectance in the visible, near-infrared, and shortwave bands, and also to five spectral vegetation indices (SVIs): Normalised Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Atmospherically Resistant Vegetation Index (ARVI). The three sensor types show the same predictive ability for stand LAI, with an uncertainty of about 1.0m2/m2 for LAI between 0.5 and 6.9m2/m2. For each sensor type, the strength of the empirical relationship between LAI and NDVI remains the same, regardless of the image processing level considered [digital counts, radiances using calibration coefficients for each sensor, top of atmosphere (TOA), and top of canopy (TOC) reflectances]. On the other hand, NDVIs based on radiance, TOA reflectance, and TOC reflectance, determined from IKONOS radiometric data, are systematically lower than from SPOT and ETM+ data. The offset is approximately 0.11 NDVI units for radiance and TOA reflectance-based NDVI, and approximately 0.20 NDVI units after atmospheric corrections. The same conclusions were observed using the other indices. SVIs using IKONOS data are always lower than those computed using ETM+ and SPOT data. Factors that may explain this behavior were investigated. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (i.e., PROSAIL), we show that the spectral response in radiance of IKONOS sensor in the red band is the main factor explaining the differences in SVIs between IKONOS and the other two sensors. Finally, we conclude that, for bare soils or very sparse vegetation, radiometric data acquired by IKONOS, SPOT, and ETM+ are similar and may be used without any correction. For surfaces covered with dense vegetation, a negative offset of 10% of IKONOS NDVI should be considered