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.
2022
Abstract
The visual impacts of landscape change are important for how people perceive landscapes and whether they consider changes to be positive or negative. Landscape photographs and photographs of landscape elements may capture information about the visual qualities of landscapes and can also be used to illustrate, and even to quantify, how these visual qualities change over time. We developed a methodology for a monitoring scheme, based on taking photographs from exactly the same locations at different points in time. We tested two methods: one where fieldworkers chose freely the location and direction of photographs, and one where photo locations and four out of five directions were predefined. We found that the method using predefined locations provided a representative sample of the visual qualities present in the landscape and was relatively person-independent but missed rare landscape components. The method using free selection of photo locations and directions captured rarities, but the content of the photos varied from photographer to photographer. Considering the strengths and weaknesses of the two approaches, we recommend a method that combines aspects of both when establishing a monitoring scheme based on repeat photography, with predefined locations to ensure that the entire area is covered, and additional freely chosen photo locations to capture special subject matter that would otherwise be missed.
Authors
Axel Mithöfer Michael Riemann Corine Alexis Faehn Anna Mrazova Laura JaakolaAbstract
Global warming is predicted to change the growth conditions for plants and crops in regions at high latitudes (>60° N), including the Arctic. This will be accompanied by alterations in the composition of natural plant and pest communities, as herbivorous arthropods will invade these regions as well. Interactions between previously non-overlapping species may occur and cause new challenges to herbivore attack. However, plants growing at high latitudes experience less herbivory compared to plants grown at lower latitudes. We hypothesize that this finding is due to a gradient of constitutive chemical defense towards the Northern regions. We further hypothesize that higher level of defensive compounds is mediated by higher level of the defense-related phytohormone jasmonate. Because its biosynthesis is light dependent, Arctic summer day light conditions can promote jasmonate accumulation and, hence, downstream physiological responses. A pilot study with bilberry (Vaccinium myrtillus) plants grown under different light regimes supports the hypothesis.
Authors
Kemeng Xiao Karin Juul Hesselsøe Christer Magnusson Tatsiana Espevig Trond Olav Pettersen Trygve S. AamlidAbstract
No abstract has been registered
Abstract
Plant selection for rain gardens along streets and roads in cold climates can be complicated, as the plants are subjected to combined stresses including periodic inundation, de-icing salts, road dust, splashes of water from the road, freezing and thawing of soil, and periods with ice cover during the winter. The purpose of this study was to identify species suited to grow in these conditions and determine their optimal placement within roadside rain gardens. Thirty-one herbaceous perennial species and cultivars were planted in real-scale rain gardens in a street in Drammen (Norway) with supplemental irrigation, and their progress was recorded during the following three growing seasons. The study highlights considerable differences between species’ adaptation to roadside rain gardens in cold climates, especially closest to the road. Some candidate species/cultivars had a high survival rate in all rain garden positions and were developed well. These were: Amsonia tabernaemontana, Baptisia australis, Calamagrostis × acutiflora ‘Overdam’, Hemerocallis ‘Camden Gold Dollar’, Hemerocallis ‘Sovereign’, Hemerocallis lilioasphodelus, Hosta ‘Sum & Substance’, Iris pseudacorus and Liatris spicata ‘Floristan Weiss’. Other species/cultivars appeared to adapt only to certain parts of the rain garden or had medium tolerance. These were: Calamagrostis brachytricha, Carex muskingumensis, Eurybia × herveyi ‘Twilight’, Hakonechloa macra, Hosta ‘Francee’, Hosta ‘Striptease’, Liatris spicata ‘Alba’, Lythrum salicaria ‘Ziegeunerblut’, Molinia caerulea ‘Moorhexe’, Molinia caerulea ‘Overdam’, and Sesleria autumnalis. Species/cultivars that showed high mortality and poor development at all rain garden positions should be avoided in roadside cold climate rain gardens. These include Amsonia orientalis, Aster incisus ‘Madiva’, Astilbe chinensis var. tacquettii ‘Purpurlanze’, Chelone obliqua, Dryopteris filix-mas, Eurybia divaricata, Geranium ‘Rozanne’, Helenium ‘Pumilum Magnificum’, Luzula sylvatica, Polygonatum multiflorum and Veronicastrum virginicum ‘Apollo’. The study also found considerable differences between cultivars within the same species, especially for Hosta cvv. and Liatris spicata. Further investigations are needed to identify the cultivars with the best adaption to roadside rain gardens in cold climates.
Authors
Yi Zhang Linhui Li Zhonghao Ren Yating Yu Yeqing Li Junting Pan Yanjuan Lu Lu Feng Weijin Zhang Yongming HanAbstract
The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23–45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750–3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
Authors
Jeanne Portier Florian Zellweger Jürgen Zell Iciar Alberdi Asensio Michal Bosela Johannes Breidenbach Vladimír Šebeň Rafael O. Wüest Brigitte RohnerAbstract
To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.
Authors
Pia Heltoft Thomsen Gudni Thorvaldsson Anne Mette Dahl Jensen Tatsiana Espevig Karin Juul Hesselsøe Wendy Marie Waalen Torben K. Petersen Trond Olav Pettersen Jan Tangsveen Per Sørensen Tanja Gneist Bjarni Hannesson Kristine Sundsdal Trygve S. AamlidAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Linn VassvikAbstract
No abstract has been registered
Abstract
The diversity and abundance of pollinating insects is declining on a global scale and urgent action is needed. This is a brief film about the importance of pollinators, what is being done in Norway to counteract pollinator decline, and how you can help. Together, we can make a difference.