Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2019
Sammendrag
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Forfattere
Anna Birgitte MilfordSammendrag
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Forfattere
Trygve S. AamlidSammendrag
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Forfattere
Trygve S. AamlidSammendrag
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Sammendrag
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Sammendrag
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Forfattere
Grete H. M. JørgensenSammendrag
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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.
Sammendrag
Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution in addressing the problem without increasing workload complexity for the machine operator. In this study we developed and evaluated an approach based on RGB images to automatically detect tree-stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps to three classes of infestation; rot = 0%, 0% < rot < 50% and rot >= 50%. In this work we used deep learning approaches and conventional machine learning algorithms for detection and classification tasks. The results showed that tree-stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with root and butt-rot were correctly classified with accuracy of 83.5% and 77.5%. Classifying rot to three classes resulted in 79.4%, 72.4% and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50% and rot >= 50\%, respectively. With some modifications, the algorithm developed could be used either during the harvesting operation to detect RBR regions on the tree-stumps or as a RBR detector for post-harvest assessment of tree-stumps and logs.