Nitrogen but not phosphorus addition affects symbiotic N2 fixation by legumes in natural and semi‑natural grasslands located on four continents

The amount of nitrogen (N) derived from symbiotic N2 fixation by legumes in grasslands might be affected by anthropogenic N and phosphorus (P) inputs, but the underlying mechanisms are not known. Methods We evaluated symbiotic N2 fixation in 17 natural and semi-natural grasslands on four continents that are subjected to the same full-factorial N and P addition experiment, using the 15N natural abundance method. Results N as well as combined N and P (NP) addition reduced aboveground legume biomass by 65% and 45%, respectively, compared to the control, whereas P addition had no significant impact. Addition of N and/or P had no significant effect on the symbiotic N2 fixation per unit legume biomass. In consequence, the amount of N fixed annually per grassland area was less than half in the N addition treatments compared to control and P addition, irrespective of whether the dominant legumes were annuals or perennials. Conclusion Our results reveal that N addition mainly impacts symbiotic N2 fixation via reduced biomass of legumes rather than changes in N2 fixation per unit legume biomass. The results show that soil N enrichment by anthropogenic activities significantly reduces N 2 fixation in grasslands, and these effects cannot be reversed by additional P amendment.EEA Santa CruzFil: Vázquez, Eduardo. University of Bayreuth. Department of Soil Ecology. Bayreuth Center of Ecology and Environmental Research (BayCEER); AlemaniaFil: Vázquez, Eduardo. Swedish University of Agricultural Sciences. Department of Soil and Environment; SueciaFil: Schleuss, Per‑Marten. University of Bayreuth. Department of Soil Ecology. Bayreuth Center of Ecology and Environmental Research (BayCEER); AlemaniaFil: Borer, Elizabeth T. University of Minnesota. Department of Ecology, Evolution, and Behavior; Estados UnidosFil: Bugalho, Miguel N. University of Lisbon. Centre for Applied Ecology “Prof. Baeta Neves” (CEABN-InBIO). School of Agriculture; Portugal.Fil: Caldeira, Maria. C. University of Lisbon. Forest Research Centre. School of Agriculture; Portugal.Fil: Eisenhauer, Nico. German Centre for Integrative Biodiversity Research; AlemaniaFil: Eisenhauer, Nico. Leipzig University. Institute of Biology; AlemaniaFil: Eskelinen, Anu. German Centre for Integrative Biodiversity Research; AlemaniaFil: Eskelinen, Anu. Physiological Diversity, Helmholtz Centrefor Environmental Research; AlemaniaFil: Eskelinen, Anu. University of Oulu. Ecology & Genetics; FinlandiaFil: Fay, Philip A. Grassland Soil and Water Research Laboratory (USDA-ARS); Estados UnidosFil: Haider, Sylvia. German Centre for Integrative Biodiversity Research; AlemaniaFil: Haider, Sylvia. Martin Luther University. Institute of Biology. Geobotany and Botanical Garden; AlemaniaFil: Jentsch, Anke. University of Bayreuth. Department of Soil Ecology. Bayreuth Center of Ecology and Environmental Research (BayCEER); AlemaniaFil: Kirkman, Kevin P. University of KwaZulu-Natal. School of Life Sciences; SudáfricaFil: McCulley, Rebecca L. University of Kentucky. Department of Plant and Soil Sciences; Estados UnidosFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Price, Jodi. Charles Sturt University. Institute for Land, Water and Society; Australia.Fil: Richards, Anna E. CSIRO Land and Water. Northern Territory; Australia.Fil: Risch, Anita C. Swiss Federal Institute for Forest, Snow and Landscape Research WSL; SuizaFil: Roscher, Christiane. German Centre for Integrative Biodiversity Research; AlemaniaFil: Roscher, Christiane. Physiological Diversity, Helmholtz Centre for Environmental Research; AlemaniaFil: Schütz, Martin. Swiss Federal Institute for Forest, Snow and Landscape Research WSL; SuizaFil: Seabloom, Eric William. University of Minnesota. Dept. of Ecology, Evolution, and Behavior; Estados UnidosFil: Standish, Rachel J. Murdoch University. Harry Butler Institute; Australia.Fil: Stevens, Carly J. Lancaster University. Lancaster Environment Centre; Reino UnidoFil: Tedder, Michelle J. University of KwaZulu-Natal. School of Life Sciences; SudáfricaFil: Virtanen, Risto. University of Oulu. Ecology & Genetics; Finlandia.Fil: Spohn, Marie. University of Bayreuth. Department of Soil Ecology. Bayreuth Center of Ecology and Environmental Research (BayCEER); AlemaniaFil: Spohn, Marie. Swedish University of Agricultural Sciences. Department of Soil and Environment; Sueci

Multiple Facets of Biodiversity Drive the Diversity-Stability Relationship

A significant body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity–stability relationship remains unclear. Here we used data from 39 biodiversity experiments and structural equation modeling to investigate the roles of species richness, phylogenetic diversity, and both the diversity and community-weighted mean of functional traits representing the ‘fast–slow’ leaf economics spectrum in driving the diversity–stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony. Contrary to our hypothesis, low phylogenetic diversity also enhances ecosystem stability directly, albeit weakly. While the diversity of fast–slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our results demonstrate that biodiversity influences ecosystem stability via a variety of facets, thus highlighting a more multicausal relationship than has been previously acknowledged

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

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant

Genetic differentiation and phenotypic plasticity jointly shape intraspecific trait variation, but their roles differ among traits. In short-lived plants, reproductive traits may be more genetically determined due to their impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with observational field data throughout the range of a widespread short-lived herb, Plantago lanceolata, we (1) disentangled genetic and plastic responses of functional traits to a set of environmental drivers and (2) assessed how genetic differentiation and plasticity shape observational trait-environment relationships. Reproductive traits showed distinct genetic differentiation that largely determined observational patterns, but only when correcting traits for differences in biomass. Vegetative traits showed higher plasticity and opposite genetic and plastic responses, masking the genetic component underlying field-observed trait variation. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related to fitness