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International audience; The theory of traits (life-history, ecological and biological traits) states that a species' characteristics might enable its persistence and development in given environmental conditions. If environment is the major factor controlling functional assemblage structure, species with similar attributes are expected to inhabit a similar environment. This study uses trait states in 849 European riverine fish assemblages to analyze the influence of environment, phylogeny and biogeography on the functional structure of these assemblages. European fish assemblages were highly structured and two main syndromes (a suite of coevolved traits) were observed: (1) assemblages dominated by stenothermal intolerant individuals and (2) assemblages dominated by eurythermal, eurytopic and tolerant individuals. Temperature and stream physical structure were the two main environmental factors explaining the diversity of fish assemblage functional structures, while the influence of biogeographic factors was weak, once environment was taken into account. This suggests that, whatever the regional species pool, similar assemblage functional structures will be found in similar environmental conditions. The phylogenetic relatedness between species might also explain to some extent the associations between the species traits observed among European fish assemblages.

We present an estimate of net ecosystem exchange (NEE) of CO2 in Europe for the years 2001-2007. It is derived with a data assimilation that uses a large set of atmospheric CO2 mole fraction observations (∼70 000) to guide relatively simple descriptions of terrestrial and oceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrial sources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems across Europe in which prescribed climate, weather, and surface characteristics introduce finer scale gradients. We find that the terrestrial biosphere in Europe absorbed a net average of -165 Tg C yr-1 over the period considered. This uptake is predominantly in non-EU countries, and is found in the northern coniferous (-94 Tg C yr-1) and mixed forests (-30 Tg C yr-1) as well as the forest/field complexes of eastern Europe (-85 Tg C yr-1). An optimistic uncertainty estimate derived using three biosphere models suggests the uptake to be in a range of -122 to -258 Tg C yr-1, while a more conservative estimate derived from the a-posteriori covariance estimates is -165±437 Tg C yr-1. Note, however, that uncertainties are hard to estimate given the nature of the system and are likely to be significantly larger than this. Interannual variability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent with increased seasonal cycles of observed CO2. We speculate these changes to result from the strong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summer growth conditions, and altered horizontal and vertical mixing in the atmosphere. All our results are available through http://www.carbontracker.eu. © 2009 Blackwell Publishing Ltd.

National and sub-national scale estimates of soil organic carbon (SOC) stocks and changes can provide information land degradation risk, C sequestration possibilities and the potential sustainability of proposed land management plans. Under a GEF co-financed project, ‘The GEFSOC Modelling System’ was used to determine SOC stocks and projected stock change rates for four case study areas; The Brazilian Amazon, The Indo-Gangetic Plains of India, Kenya and Jordan. Each case study represented soil and vegetation types, climates and land management systems that are under represented globally, in terms of an understanding of land use and land management systems and the effects these systems have on SOC stocks. The stocks and stock change rates produced were based on detailed geo-referenced datasets of soils, climate, land use and management information. These datasets are unique as they bring together national and regional scale data on the main variables determining SOC, for four contrasting non-temperate eco-regions. They are also unique, as they include information on land management practices used in subsistence agriculture in tropical and arid areas. Implications of a greater understanding of SOC stocks and stock change rates in non-temperate areas are considered. Relevance to national land use plans are explored for each of the four case studies, in terms of sustainability, land degradation and greenhouse gas mitigation potential. Ways in which such information will aid the case study countries in fulfilling obligations under the United Nations Conventions on Climate Change, Biodiversity and Land Degradation are also considered. The need for more detailed land management data to improve SOC stock estimates in non-temperate areas is discussed.

The common GIS-based approach to regional analyses of soil organic carbon (SOC) stocks and changes is to define geographic layers for which unique sets of driving variables are derived, which include land use, climate, and soils. These GIS layers, with their associated attribute data, can then be fed into a range of empirical and dynamic models. Common methodologies for collating and formatting regional data sets on land use, climate, and soils were adopted for the project Assessment of Soil Organic Carbon Stocks and Changes at National Scale (GEFSOC). This permitted the development of a uniform protocol for handling the various input for the dynamic GEFSOC Modelling System. Consistent soil data sets for Amazon-Brazil, the Indo-Gangetic Plains (IGP) of India, Jordan and Kenya, the case study areas considered in the GEFSOC project, were prepared using methodologies developed for the World Soils and Terrain Database (SOTER). The approach involved three main stages: (1) compiling new soil geographic and attribute data in SOTER format; (2) using expert estimates and common sense to fill selected gaps in the measured or primary data; (3) using a scheme of taxonomy-based pedotransfer rules and expert-rules to derive soil parameter estimates for similar soil units with missing soil analytical data. The most appropriate approach varied from country to country, depending largely on the overall accessibility and quality of the primary soil data available in the case study areas. The secondary SOTER data sets discussed here are appropriate for a wide range of environmental applications at national scale. These include agro-ecological zoning, land evaluation, modelling of soil C stocks and changes, and studies of soil vulnerability to pollution. Estimates of national-scale stocks of SOC, calculated using SOTER methods, are presented as a first example of database application. Independent estimates of SOC stocks are needed to evaluate the outcome of the GEFSOC Modelling System for current conditions of land use and climate.

National-scale inventories of soil organic carbon (SOC) and forest floor carbon (FFC) stocks have a high uncertainty. Inventories are often based on the interpolation of sampled information, often using a number of covariables to help such interpolation. The rationale for the choice of these covariables is not always documented, despite the fact that many local-scale studies have identified the factors explaining spatial variability of SOC and FFC stocks. These studies indicate, among others the importance of long-term land use history. Despite this, information on the effects of land use history has never been used to explain variability of carbon stocks in national-scale inventories. We designed an alternative method to improve national-scale inventories of SOC and FCC for the Dutch sand area that takes stock of the findings of detailed case studies. Determinants for SOC and FFC stocks derived from landscape-scale case studies were used to map national-scale spatial variability and to calculate national totals. The resulting national-scale spatial distribution was compared with the SOC stock map from the current Dutch greenhouse gas inventory. Using land use history to explain SOC variability decreased the error of the SOC stock estimate in 60 % of the area. The error in FFC stocks decreased in half of the forest area after including soil fertility, tree species, and forest age as explanatory factors. Estimates with reduced uncertainty will make land use and land management a more attractive and acceptable mitigation option to reduce emissions of greenhouse gases for the LULUCF sector. © 2012 Springer Science+Business Media New York.

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in arid areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use.

Currently we have little understanding of the impacts of land use change on soil C stocks in the Brazilian Amazon. Such information is needed to determine impacts on the global C cycle and the sustainability of agricultural systems that are replacing native forest. The aim of this study was to predict soil carbon stocks and changes in the Brazilian Amazon during the period between 2000 and 2030, using the GEFSOC soil carbon (C) modelling system. In order to do so, we devised current and future land use scenarios for the Brazilian Amazon, taking into account: (i) deforestation rates from the past three decades, (ii) census data on land use from 1940 to 2000, including the expansion and intensification of agriculture in the region, (iii) available information on management practices, primarily related to well managed pasture versus degraded pasture and conventional systems versus no-tillage systems for soybean ( Glycine max ) and (iv) FAO predictions on agricultural land use and land use changes for the years 2015 and 2030. The land use scenarios were integrated with spatially explicit soils data (SOTER database), climate, potential natural vegetation and land management units using the recently developed GEFSOC soil C modelling system. Results are presented in map, table and graph form for the entire Brazilian Amazon for the current situation (1990 and 2000) and the future (2015 and 2030). Results include soil organic C (SOC) stocks and SOC stock change rates estimated by three methods: (i) the Century ecosystem model, (ii) the Rothamsted C model and (iii) the intergovernmental panel on climate change (IPCC) method for assessing soil C at regional scale. In addition, we show estimated values of above and belowground biomass for native vegetation, pasture and soybean. The results on regional SOC stocks compare reasonably well with those based on mapping approaches. The GEFSOC system provided a means of efficiently handling complex interactions among biotic-edapho-climatic conditions (>363,000 combinations) in a very large area (∼500 Mha) such as the Brazilian Amazon. All of the methods used showed a decline in SOC stock for the period studied; Century and RothC simulated values for 2030 being about 7% lower than those in 1990. Values from Century and RothC (30,430 and 25,000 Tg for the 0–20 cm layer for the Brazilian Amazon region were higher than those obtained from the IPCC system (23,400 Tg in the 0–30 cm layer). Finally, our results can help understand the major biogeochemical cycles that influence soil fertility and help devise management strategies that enhance the sustainability of these areas and thus slow further deforestation.

Soil organic carbon (SOC) plays a vital role in ecosystem function, determining soil fertility, water holding capacity and susceptibility to land degradation. In addition, SOC is related to atmospheric CO, levels with soils having the potential for C release or sequestration, depending on land use, land management and climate. The United Nations Convention on Climate Change and its Kyoto Protocol, and other United Nations Conventions to Combat Desertification and on Biodiversity all recognize the importance of SOC and point to the need for quantification of SOC stocks and changes. An understanding of SOC stocks and changes at the national and regional scale is necessary to further our understanding of the global C cycle, to assess the responses of terrestrial ecosystems to climate change and to aid policy makers in making land use/management decisions. Several studies have considered SOC stocks at the plot scale, but these are site specific and of limited value in making inferences about larger areas. Some studies have used empirical methods to estimate SOC stocks and changes at the regional scale, but such studies are limited in their ability to project future changes, and most have been carried out using temperate data sets. The computational method outlined by the Intergovernmental Panel on Climate Change (IPCC) has been used to estimate SOC stock changes at the regional scale in several studies, including a recent study considering five contrasting eco regions. This 'one step' approach fails to account for the dynamic manner in which SOC changes are likely to occur following changes in land use and land management. A dynamic modelling approach allows estimates to be made in a manner that accounts for the underlying processes leading to SOC change. Ecosystem models, designed for site scale applications can be linked to spatial databases, giving spatially explicit results that allow geographic areas of change in SOC stocks to be identified. Some studies have used variations on this approach to estimate SOC stock changes at the sub-national and national scale for areas of the USA and Europe and at the watershed scale for areas of Mexico and Cuba. However, a need remained for a national and regional scale, spatially explicit system that is generically applicable and can be applied to as wide a range of soil types, climates and land uses as possible. The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System was developed in response to this need. The GEFSOC system allows estimates of SOC stocks and changes to be made for diverse conditions, providing essential information for countries wishing to take part in an emerging C market, and bringing us closer to an understanding of the future role of soils in the global C cycle. (C) 2007 Elsevier B.V. All rights reserved.

Land use and land cover changes in the Brazilian Amazon have major implications for regional and global carbon (C) cycling. Cattle pasture represents the largest single use (about 70%) of this once-forested land in most of the region. The main objective of this study was to evaluate the accuracy of the RothC and Century models at estimating soil organic C (SOC) changes under forest-to-pasture conditions in the Brazilian Amazon. We used data from 11 site-specific 'forest to pasture' chronosequences with the Century Ecosystem Model (Century 4.0) and the Rothamsted C Model (RothC 26.3). The models predicted that forest clearance and conversion to well managed pasture would cause an initial decline in soil C stocks (0-20 cm depth), followed in the majority of cases by a slow rise to levels exceeding those under native forest. One exception to this pattern was a chronosequence in Suia-Missu, which is under degraded pasture. In three other chronosequences the recovery of soil C under pasture appeared to be only to about the same level as under the previous forest. Statistical tests were applied to determine levels of agreement between simulated SOC stocks and observed stocks for all the sites within the 11 chronosequences. The models also provided reasonable estimates (coefficient of correlation = 0.8) of the microbial biomass C in the 0-10 cm soil layer for three chronosequences, when compared with available measured data. The Century model adequately predicted the magnitude and the overall trend in delta C-13 for the six chronosequences where measured 813 C data were available. This study gave independent tests of model performance, as no adjustments were made to the models to generate outputs. Our results suggest that modelling techniques can be successfully used for monitoring soil C stocks and changes, allowing both the identification of current patterns in the soil and the projection of future conditions. Results were used and discussed not only to evaluate soil C dynamics but also to indicate soil C sequestration opportunities for the Brazilian Amazon region. Moreover, modelling studies in these 'forest to pasture' systems have important applications, for example, the calculation of CO, emissions from land use change in national greenhouse gas inventories. (0 2007 Elsevier B.V. All rights reserved.

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

We present the most comprehensive pan-European assessment of future changes in cropland and grassland soil organic carbon (SOC) stocks to date, using a dedicated process-based SOC model and state-of-the-art databases of soil, climate change, land-use change and technology change. Soil carbon change was calculated using the Rothamsted carbon model on a European 10 x 10' grid using climate data from four global climate models implementing four Intergovernmental Panel on Climate Change (IPCC) emissions scenarios (SRES). Changes in net primary production (NPP) were calculated by the Lund-Potsdam-Jena model. Land-use change scenarios, interpreted from the narratives of the IPCC SRES story lines, were used to project changes in cropland and grassland areas. Projections for 1990-2080 are presented for mineral soil only. Climate effects (soil temperature and moisture) will tend to speed decomposition and cause soil carbon stocks to decrease, whereas increases in carbon input because of increasing NPP will slow the loss. Technological improvement may further increase carbon inputs to the soil. Changes in cropland and grassland areas will further affect the total soil carbon stock of European croplands and grasslands. While climate change will be a key driver of change in soil carbon over the 21st Century, changes in technology and land-use change are estimated to have very significant effects. When incorporating all factors, cropland and grassland soils show a small increase in soil carbon on a per area basis under future climate (1-7 t C ha(-1) for cropland and 3-6 t C ha(-1) for grassland), but when the greatly decreasing area of cropland and grassland are accounted for, total European cropland stocks decline in all scenarios, and grassland stocks decline in all but one scenario. Different trends are seen in different regions. For Europe (the EU25 plus Norway and Switzerland), the cropland SOC stock decreases from 11 Pg in 1990 by 4-6 Pg (39-54%) by 2080, and the grassland SOC stock increases from 6 Pg in 1990 to 1.5 Pg (25%) under the B1 scenario, but decreases to 1-3 Pg (20-44%) under the other scenarios. Uncertainty associated with the land-use and technology scenarios remains unquantified, but worst-case quantified uncertainties are 22.5% for croplands and 16% for grasslands, equivalent to potential errors of 2.5 and 1 Pg SOC, respectively. This is equivalent to 42-63% of the predicted SOC stock change for croplands and 33-100% of the predicted SOC stock change for grasslands. Implications for accounting for SOC changes under the Kyoto Protocol are discussed.