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.
2018
Sammendrag
Root rot in Norway spruce (Picea abies (L.) Karst.) causes substantial economic losses to the forestry sector. In this study, we developed a probability model for decay at breast height utilizing 18,141 increment cores sampled on temporary plots of the Norwegian National Forest Inventory. The final model showed a good fit to the data and retained significant relationships between decay and a suite of tree, stand and site variables, including diameter at breast height, stand age, altitude, growing season temperature sum (threshold 5°C), and vegetation type. By comparing model predictions with recorded decay at stump height in an independent data set, we estimated a proportionality function to adjust for the inherent underestimation of total rot that will be obtained by applying a probability model derived from increment cores sampled at breast height. We conclude that the developed model is appropriate for national and regional scenario analyses in Norway, and could also be useful as a tool for operational forestry planning. This would however require further testing on independent data, to assess how well the new model predicts decay at local scales.
Forfattere
Nga Nguyen Marko Suokas Katja Karppinen Jaana Vuosku Laura Jaakola Hely HäggmanSammendrag
Source at <a href=https://doi.org/10.1038/s41598-018-28158-7> https://doi.org/10.1038/s41598-018-28158-7</a>.
Forfattere
Anestis Karkanis Georgia Ntatsi Liga Lepse Juan A. Fernández Ingunn M. Vågen Boris Rewald Ina Alsina Arta Kronberga Astrit Balliu Margit Olle Gernot Bodner Laila Dubova Eduardo Rosa Dimitrios SavvasSammendrag
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Sammendrag
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Sammendrag
Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise.We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.
Forfattere
Matthew Schwarzkopf Michael Burnard Viacheslav Tverezovskiy Andreas Treu Miha Humar Andreja KutnarSammendrag
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Sammendrag
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