Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2016
Abstract
No abstract has been registered
Authors
Sebastian Sippel Holger Lange Miguel D. Mahecha Michael Hauhs Paul Bodesheim Thomas W. Kaminski Fabian Gans Osvaldo A. RossoAbstract
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.
Authors
Juliana PerminowAbstract
No abstract has been registered
Authors
Augustine Arukwe Camilla Catarci Carteney Monica Möder Alberto Bonini Morten Andre Maubach Trine EggenAbstract
Following the ban of polybrominated diphenyl ether (PBDEs) flame retardants under well-documented toxicity issues, organophosphate such as tris(2-butoxyethyl) phosphate (TBOEP) and tris(2-cloroethyl) phosphate (TCEP) were considered as potential substitutes. Although TBOEP and TCEP are consistently detected in the aquatic environment, there are few data about the possible toxicological effects of these compounds on aquatic organisms, including fish. In the present study, we have investigated the influence of TBOEP and TCEP on neuro- and interrenal steroidogenesis of juvenile Atlantic salmon (Salmo salar), after a seven-day exposure to four different concentrations (0 (control), 0.04, 0.2 and 1 mg/L) of each compound. TBOEP and TCEP were diluted in Milli-Q water. The expression of genes involved in ster- oidogenesis (StAR, cyp19a, cyp19b, cholesterol side-chain cleavage enzyme (P450scc), 3β-hydroxysteroid dehydrogenase (3β-hsd), and 11β-hydroxylase (cyp11β)), were analyzed in the brain and head kidney using real-time PCR. Plasma 11-ketotestosterone (11-KT) analysis was performed using enzyme im- munoassay (EIA). Our results showed that TBOEP accumulated more rapidly than TCEP in fish muscle tissue. Surprisingly, TBOEP produced less pronounced effects than TCEP on neural and interrenal ster- oidogenic responses, despite the observed rapid uptake and bioaccumulation pattern. Specifically, TBOEP produced significant and consistent concentration-specific alterations on neural- and interrenal ster- oidogenesis. Plasma levels of 11-KT were not significantly altered by any of the exposures. The increased expression of steroidogenic genes demonstrated in the present study could produce time-specific al- terations in the production of glucocorticoids and steroid hormones that play integral roles in fish me- tabolism, stress responses and adaptation, sexual maturation, reproduction and migration with overt consequences on reproductive success and survival.
Poster – Differentiating causes for erosion at the catchment scale
Robert Barneveld, Inga Greipsland
Abstract
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Authors
Kirsten TørresenAbstract
No abstract has been registered
Abstract
Abstract Questions Vegetation mapping based on field surveys is time-consuming and expensive. Distribution modelling might be used to overcome these challenges. What is the performance of distribution modelling of vegetation compared to traditional vegetation mapping when projected locally? Does the modelling performance vary among ecosystems? Does vegetation type distribution and abundance influence the modelling performance? Location Gravfjellet, Øystre Slidre commune, southern Norway. Methods Two comparable neighbouring areas, each of 4 km2, were mapped for species-defined vegetation types. One area was used for model training, the other for model projection. Maximum entropy models were run for six vegetation types, two from each of the ecosystems present in the area: forest, wetland and mountain heath- and shrublands. For each ecosystem, one locally abundant and one locally rare vegetation type were tested. AUC, the area under the receiver operating curve, was used as the model selection criterion. Environmental variables (n = 9) were selected through a backwards selection scheme, and model complexity was kept low. The models were evaluated using independent data. Results Distribution modelling of vegetation types by local projection gave high AUC values, and the results were supported by the evaluation using independent data. The modelling ability was not affected by ecosystem differences. A negative relationship between the number of points used to train the models and the AUC value before evaluation suggests that models for locally rare vegetation types had better predictive performance than the models for abundant types. This result was not significant after evaluation. Conclusion Provided that relevant explanatory variables are available at an appropriate scale, and that field-validated training points are available, distribution modelling can be used for local projection of the six tested vegetation types from the boreal–alpine ecotone.