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.
2019
Forfattere
Sølvi Wehn Anna Westin Line Johansen Anamaria Iuga Cosmin Marius Ivascu Eveliina Kallioniemi Tommy LennartssonSammendrag
Semi-natural grasslands are hotspots of biodiversity in Europe and provide amounts of flower resources for pollinators. We present data on composition and spatial turnover of herb species and flower resources in and between semi-natural grasslands in Romania mown at different times during the growth season (early, intermediate, late). The data include herb species occurrences, their phenological stage, flower resources, and measures of spatial turnover of the species occurrences and flower resources based on Detrended Correspondence Analyses (DCA), in the start of August. The dataset is provided as supplementary material and associated with the research article “Traditional semi-natural grassland management with heterogeneous mowing times enhances flower resources for pollinators in agricultural landscapes” [1] Johansen et al.. See Johansen et al. for data interpretation.
Sammendrag
No abstract has been registered
Forfattere
Amy Elizabeth Eycott Sylwia Wierzcholska Anna Lubek Martin Kukwa Wojciech Adamowski Fride Høistad Schei Bogdan JaroszewiczSammendrag
No abstract has been registered
Sammendrag
No abstract has been registered
Forfattere
Lalisa A. Duguma Joanes Atela Peter A. Minang Alemayehu N. Ayana Belachew Gizachew Zeleke Judith M. Nzyoka Florence BernardSammendrag
Deforestation and forest degradation (D&D) in the tropics have continued unabated and are posing serious threats to forests and the livelihoods of those who depend on forests and forest resources. Smallholder farmers are often implicated in scientific literature and policy documents as important agents of D&D. However, there is scanty information on why smallholders exploit forests and what the key drivers are. We employed behavioral sciences approaches that capture contextual factors, attitudinal factors, and routine practices that shape decisions by smallholder farmers. Data was collected using household surveys and focus group discussions in two case study forests—Menagesha Suba Forest in Ethiopia and Maasai Mau Forest in Kenya. Our findings indicate that factors that forced farmers to engage in D&D were largely contextual, i.e., sociodemographic, production factors constraint, as well as policies and governance issues with some influences of routine practices such as wood extraction for fuelwood and construction. Those factors can be broadly aggregated as necessity-driven, market-driven, and governance-driven. In the forests studied, D&D are largely due to necessity needs and governance challenges. Though most factors are intrinsic to smallholders’ context, the extent and impact on D&D were largely aggravated by factors outside the forest landscape. Therefore, policy efforts to reduce D&D should carefully scrutinize the context, the factors, and the associated enablers to reduce forest losses under varying socioeconomic, biophysical, and resource governance conditions.
Forfattere
Ole Martin Eklo Marit Almvik Halvard Hole Åge Arild Nyborg Marianne Stenrød Katrine Borgå Hubert Dirven Merete Grung Jan Ludvig Lyche Marit Låg Asbjørn Magne Nilsen Line Emilie Sverdrup Torsten KällqvistSammendrag
No abstract has been registered
Forfattere
David M. Gadoury Andrew Bierman Mark Rea Arne Stensvand Aruppillai Suthaparan Rodrigo B. Onofre Natalia A. PeresSammendrag
No abstract has been registered
Forfattere
Lisa Kvalbein Ida Lereng Anne-Grete Roer Hjelkrem Anne Kjersti Bakken Josefine Helene Krogh Selj Jonathan FagerströmSammendrag
No abstract has been registered
Sammendrag
Invasive alien species and new plant pests are introduced into new regions at an accelerating rate, due to increasing international trade with soil, plants and plant products. Exotic, plant pathogenic oomycetes in soil from the root zone of imported plants pose a great threat to endemic ecosystems and horticultural production. Detecting them via baiting and isolation, with subsequent identification of the isolated cultures by Sanger sequencing, is labour intensive and may introduce bias due to the selective baiting process. We used metabarcoding to detect and identify oomycetes present in soil samples from imported plants from six different countries. We compared metabarcoding directly from soil both before and after baiting to a traditional approach using Sanger-based barcoding of cultures after baiting. For this, we developed a standardized analysis workflow for Illumina paired-end oomycete ITS metabarcodes that is applicable to future surveillance efforts. In total, 73 soil samples from the rhizosphere of woody plants from 33 genera, in addition to three samples from transport debris, were analysed by metabarcoding the ITS1 region with primers optimized for oomycetes. We detected various Phytophthora and Pythium species, with Pythium spp. being highly abundant in all samples. We also found that the baiting procedure, which included submerging the soil samples in water, resulted in the enrichment of organisms other than oomycetes, compared to non-baited soil samples.
Forfattere
Kaiguang Zhao Michael A. Wulder Tongxi Hu Ryan Bright Qiusheng Wu Haiming Qin Yang Li Elizabeth Toman Bani Mallick Xuesong Zhang Molly BrownSammendrag
Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single “best” model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics.