Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2016
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Forfattere
Ibrahim Tahir Eivind VangdalSammendrag
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Sammendrag
Oidium neolycopersici, the cause of powdery mildew in tomato, was exposed to UV radiation from 250 to 400 nm for 1, 12, or 24 min. Radiation ≤ 280 nm strongly reduced conidial germination, hyphal expansion, penetration attempt and infection of O. neolycopersici. From 290 to 310 nm the effect depended on duration of exposure, while there was no effect ≥310 nm. There were no significant differences within the effective UV range (250–280 nm). Conidial germination on a water agar surface was b20% or around 40%, respectively, if samples were exposed for 1 min within the effective UV range followed by 24 h or 48 h incubation. Twelve or 24 min exposure reduced germination to close to nil. A similar trend occurred for germination of conidia on leaf disks on water agar in Petri dishes. The effective UV range significantly reduced all subsequent developmental stages of O. neolycopersici. There was no cytoplasmic mitochondrial streaming in conidia exposed to the effective UV range, indicating that there may be a direct effect via cell cycle arrest. There was no indication of reactive oxygen species involvement in UV mediated inhibition of O. neolycopersici. Optical properties of O. neolycopersici indicat- ed that the relative absorption of UV was high within the range of 250 to 320 nm, and very low within the range of 340 to 400 nm. Identification of UV wavelengths effective against O. neolycopersici provides a future basis for precise disease control.
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Forfattere
Sigridur DalmannsdottirSammendrag
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Forfattere
Aaron SmithSammendrag
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Forfattere
Sebastian Sippel Holger Lange Miguel D. Mahecha Michael Hauhs Paul Bodesheim Thomas W. Kaminski Fabian Gans Osvaldo A. RossoSammendrag
Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide dataanalytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.