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.
2025
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Luiz C. Garcia Carlos H. Rocha Nátali M. de Souza Pedro H. Weirich Neto Jaime A. Gomes Thiago InagakiAbstract
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Jari Hynynen Narayanan Subramanian Clara Antón Fernández Soili Haikarainen Emma Holmström Micky Allen Saija Huuskonen Jouni Siipilehto Hannu Salminen Mika Lehtonen Kjell Andreassen Urban NilssonAbstract
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Junbin Zhao Mikhail Mastepanov Cornelya Klutsch Hanna Marika Silvennoinen David Kniha Svein Wara Runar KjærAbstract
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Tatsiana Espevig Kristine Sundsdal Victoria Stornes Moen Kate Entwistle Marina Usoltseva Sabine Braitmaier Daniel Hunt Carlos Guerrero Monica Skogen Erik LysøeAbstract
Thirty-seven turfgrass samples expressing dollar spot symptoms were collected in summer 2020 on golf courses in Sweden, Denmark, United Kingdom, Germany, Portugal, and Spain. The fungi were isolated at Norwegian Institute of Bioeconomy Research (NIBIO) Turfgrass Laboratory (Norway) and sent for molecular identification using sequencing of regions of ITS (internal transcribed regions of the ribosomal DNA) and calmodulin. Clarireedia homoeocarpa was identified in four turfgrass samples and Clarireedia jacksonii was identified in 11 turfgrass samples. From seven turfgrass samples, the isolated fungi were not Clarireedia spp., but Waitea circinata, Fusarium culmorum, and Fusarium oxysporum. This suggests dollar spot is not always accurately identified from foliar symptoms in the field.
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Ruochen Li Xin Pei Ming Zhang Xuhui Deng Chengyuan Tao Jiabao Wang Xueli Chen Nicholas Clarke Lidia Sas-Paszt Zongzhuang Shen Rong Li Qirong ShenAbstract
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Lone RossAbstract
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Long-term monitoring of ecosystems is the only direct method to provide insights into the system dynamics on a range of timescales from the temporal resolution to the duration of the record. Time series of typical environmental variables reveal a striking diversity of trends, periodicities, and long-range correlations. Using several decades of observations of water chemistry in first-order streams of three adjacent catchments in the Harz mountains in Germany as example, we calculate metrics for these time series based on ordinal pattern statistics, e.g. permutation entropy and complexity, Fisher information, or q-complexity, and other indicators like Tarnopolski diagrams. The results are compared to those obtained for reference statistical processes, like fractional Brownian motion or ß noise. After detrending and removing significant periodicities from the time series, the distances of the residuals to the reference processes in this space of metrics serves as a classification of nonlinear dynamical behavior, and to judge whether inter-variable or rather inter-site differences are dominant. The classification can be combined with knowledge about the processes driving hydrochemistry, elucidating the connections between the variables. This can be the starting point for the next step, constructing causal networks from the multivariate dataset.