Long-term carbon loss in fragmented Neotropical forests

Tropical forests play an important role in the global carbon cycle, as they store a large amount of carbon (C). Tropical forest deforestation has been identified as a major source of CO2 emissions, though biomass loss due to fragmentation—the creation of additional forest edges—has been largely overlooked as an additional CO2 source. Here, through the combination of remote sensing and knowledge on ecological processes, we present long-term carbon loss estimates due to fragmentation of Neotropical forests: within 10 years the Brazilian Atlantic Forest has lost 69 (±14) Tg C, and the Amazon 599 (±120) Tg C due to fragmentation alone. For all tropical forests, we estimate emissions up to 0.2 Pg C y−1 or 9 to 24% of the annual global C loss due to deforestation. In conclusion, tropical forest fragmentation increases carbon loss and should be accounted for when attempting to understand the role of vegetation in the global carbon balance.This study was part of the project ‘Biodiversity conservation in a fragmented landscape at the Atlantic Plateau of São Paulo’ (BIOTA/Caucaia and BioCAPSP) funded by FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, project no. 99/05123-4, 01/13309-2, 02/02125-0, 02/02126-7), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, project no. 690144/01-6), Fundação O Boticário de Proteção à Natureza, and by BMBF (German Federal Ministry of Education and Research, project n. 01LB0202). J.P.M. and M.C.R. thank the Brazilian Science Council (Conselho Nacional de Desenvolvimento Científico) for his research fellowship (process no. 307934/2011-0 and 312045/2013-1, respectively). A.H. and S.P. were supported by the ERC advanced grant 233066. M.M. has been supported by BMBF (project n. 01LB0202), and the Department of Ecological Modelling of the Helmholtz Centre for Environmental Research (UFZ). We thank Birgit Felinks for the support during the Mata Atlântica project. Florian Hartig provided valuable comments on an earlier version of this manuscript. S.P. has been funded by the Helmholtz Association of German Research Centres within the project ‘Biomass and Bioenergy systems’. A.H. was also supported by the Helmholtz-Alliance Remote Sensing and Earth System Dynamics. A.H. thanks C. Wissel and H. Bossel for supporting the FORMIND project over the years

TRY plant trait database - enhanced coverage and open access

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

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

TRY plant trait database - enhanced coverage and open access

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

Temperature sensitivity of soil enzymes along an elevation gradient in the Peruvian Andes

Soil enzymes are catalysts of organic matter depolymerisation, which is of critical importance for ecosystem carbon (C) cycling. Better understanding of the sensitivity of enzymes to temperature will enable improved predictions of climate change impacts on soil C stocks. These impacts may be especially large in tropical montane forests, which contain large amounts of soil C. We determined the temperature sensitivity (Q 10) of a range of hydrolytic and oxidative enzymes involved in organic matter cycling from soils along a 1900 m elevation gradient (a 10 °C mean annual temperature gradient) of tropical montane forest in the Peruvian Andes. We investigated whether the activity (V max) of selected enzymes: (i) exhibited a Q 10 that varied with elevation and/or soil properties; and (ii) varied among enzymes and according to the complexity of the target substrate for C-degrading enzymes. The Q 10 of V max for β-glucosidase and β-xylanase increased with increasing elevation and declining mean annual temperature. For all other enzymes, including cellobiohydrolase, N-acetyl β-glucosaminidase and phosphomonoesterase, the Q 10 of V max did not vary linearly with elevation. Hydrolytic enzymes that degrade more complex C compounds had a greater Q 10 of V max, but this pattern did not apply to oxidative enzymes because phenol oxidase had the lowest Q 10 value of all enzymes studied here. Our findings suggest that regional differences in the temperature sensitivities of different enzyme classes may influence the terrestrial C cycle under future climate warming