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
Abstract
Sustainable forest management in an era of global changes has always been a central thematic area for the International Boreal Forest Research Association (IBFRA). At the 17th IBFRA conference held on 24–29 May 2015 in Rovaniemi, Finland, the theme of global change was accompanied by a new theme related to the use and value of big data in boreal forest management and research. Keynote presentations had a clear message that sustainably managed boreal forests and peatlands play a significant role in climate change mitigation. However, the choice of the most efficient mitigation options will vary with regional differences in ecology, institutional strength, and management intensity. In addition to changes in greenhouse gas fluxes linked to ecosystem dynamics, the design of climate change mitigation strategies should also account for the fate of harvested wood products and for the substitution of more energy-intensive materials such as concrete and steel. For climate change mitigation, it is therefore not only forest management that matters, but also ensuring the best possible end use for the produced biomass. Key note presentations on use and value of big data in the forest sector demonstrated the role of time series of remote sensing data in forest monitoring and research. In addition, new technologies and methods including terrestrial laser scanning are starting to provide detailed three-dimensional information from forest stands from which management tools and scientific understanding will be developed. Finally, citizen science was shown to offer a vast potential for the generation of forest-based data. Thus, new means are being developed by which forest scientists and managers will be able to obtain new, more frequent, and more detailed information on the forest. The ensuing development of knowledge will benefit the forest sector, create new opportunities for furthering boreal forest science, and finally benefit the society as a whole...
Authors
Inger Sundheim FløistadAbstract
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Abstract
Norway spruce (Picea abies (L.) Karst.) understory seedlings, growing in partially harvested plots with different canopy cover in a boreal spruce stand, were spot fertilized (Hydro 15-4-12) 9 years after planting. The principal aimwasto test the hypothesis that nitrogen (N)availability influences growthof understory seedlings at intermediate but not at lowlevels of irradiance. In addition, we tested the combined influences of N and light availability on selected morphological and phenological traits, covering a 2-year period after treatment. Diffuse radiation (DIFR) at the seedling level was estimated from hemispherical photographs and ranged from 19 to 46 per cent of DIFR in openconditions. Fertilizer applicationwasassociatedwithamarkedincreasein foliarNconcentration.Thefertilized seedlings grew better in height and root collar diameter compared with unfertilized controls. While the absolute growth in both diameterand height increased with increasing DIFR, seedlings also responded to improved nutrient availability across the rangeof light conditions studied. Fertilizer treatment did not affect thenumberof nodal buds, but we observed a higher apical dominance ratio and advanced bud burst in fertilized seedlings. In conclusion, nutrient availability influenced growth and bud phenology of understory Norway spruce seedlings at least down to 20 per cent DIFR.
Abstract
No abstract has been registered
Authors
Holger LangeAbstract
No abstract has been registered
Abstract
Convergent Cross Mapping (CCM) has recently been introduced by Sugihara et al. for the identification and quantification of causal relationships among ecosystem variables. In particular, the method allows to decide on the direction of causality; in some cases, the causality might be bidirectional, indicating a network structure. We extend this approach by introducing a method of surrogate data to obtain confidence intervals for CCM results. We then apply this method to time series from stream water chemistry. Specifically, we analyze a set of eight dissolved major ions from three different catchments belonging to the hydrological monitoring system at the Bramke valley in the Harz Mountains, Germany. Our results demonstrate the potentials and limits of CCM as a monitoring instrument in forestry and hydrology or as a tool to identify processes in ecosystem research. While some networks of causally linked ions can be associated with simple physical and chemical processes, other results illustrate peculiarities of the three studied catchments, which are explained in the context of their special history.
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
Holger LangeAbstract
No abstract has been registered
Authors
Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus ReichsteinAbstract
No abstract has been registered