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.
2020
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Lorène Julia Marchand Inge Dox Jožica Gričar Peter Prislan Sebastien Leys Jan Van den Bulcke Patrick Fonti Holger Lange Erik Matthysen Josep Peñuelas Paolo Zuccarini Matteo CampioliSammendrag
We explored the inter-individual variability in bud-burst and its potential drivers, in homogeneous mature stands of temperate deciduous trees. Phenological observations of leaves and wood formation were performed weekly from summer 2017 to summer 2018 for pedunculate oak, European beech and silver birch in Belgium. The variability of bud-burst was correlated to previous’ year autumn phenology (i.e. the onset of leaf senescence and the cessation of wood formation) and tree size but with important differences among species. In fact, variability of bud-burst was primarily related to onset of leaf senescence, cessation of wood formation and tree height for oak, beech and birch, respectively. The inter-individual variability of onset of leaf senescence was not related to the tree characteristics considered and was much larger than the inter-individual variability in bud-burst. Multi-species multivariate models could explain up to 66% of the bud-burst variability. These findings represent an important advance in our fundamental understanding and modelling of phenology and tree functioning of deciduous tree species.
Sammendrag
The rapidly expanding field of machine learning (ML) provides many methodological opportunities which match very well with the needs and challenges of hydrological research. Due to extended measurement networks, more frequent automatic measurements of hydrological variables, and not the least increasing use of remote sensing products, the era of big data surely has arrived in hydrology. Process-based models are usually developed for certain spatiotemporal scales, not fitting easily to the scope of the new datasets. Automatic methods that learn patterns and generalizations have been demonstrated to be superior in many applications. The chapter provides an overview of some of the most important machine learning algorithms which have been used in the hydrological literature. It will be shown that there is no single best method among them, but instead a spectrum of methods should be utilized, from highly flexible ones to more parsimonious learning methods, depending on the specific hydrological application, research question, and data availability. Most machine learning techniques require a calibration and a validation dataset for training. As these data are usually correlated in time and space, the problem of bias-variance tradeoff arises will be discussed as a simple example. The presentation of ML algorithms, roughly following chronological order, is discussed starting with artificial neural networks through support vector machines to gradient boosting machines. As data streams increase, these and other machine learning techniques will play an ever more important role in hydrology.
Sammendrag
Bei der Modellierung von Ökosystemen treffen zwei Seiten des technischen Fortschritts aufeinander, einerseits die neuen Möglichkeiten, vertreten durch die Informatik und die Verbreitung von Computern, andererseits die indirekten Wirkungen moderner Zivilisationen auf die Umwelt des Menschen und die Biosphäre. In diesem Beitrag geht es um die Möglichkeit einer Zusammenschau dieser zwei Seiten der Moderne vom Standpunkt der ökologischen Modellbildung.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Svein SolbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Svein SolbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Sophia Etzold Marco Ferretti Gert Jan Reinds Svein Solberg Arthur Gessler Peter Waldner Marcus Schaub David Simpson Sue Benham Karin Hansen Morten Ingerslev Mathieu Jonard Per Erik Karlsson Antti-Jussi Lindroos Aldo Marchetto Miklos Manninger Henning Meesenburg Päivi Merilä Pekka Nöjd Pasi Rautio Tanja G.M. Sanders Walter Seidling Mitja Skudnik Anne Thimonier Arne Verstraeten Lars Vesterdal Monika Vejpustkova Wim de VriesSammendrag
Changing environmental conditions may substantially interact with site quality and forest stand characteristics, and impact forest growth and carbon sequestration. Understanding the impact of the various drivers of forest growth is therefore critical to predict how forest ecosystems can respond to climate change. We conducted a continental-scale analysis of recent (1995–2010) forest volume increment data (ΔVol, m3 ha−1 yr−1), obtained from ca. 100,000 coniferous and broadleaved trees in 442 even-aged, single-species stands across 23 European countries. We used multivariate statistical approaches, such as mixed effects models and structural equation modelling to investigate how European forest growth respond to changes in 11 predictors, including stand characteristics, climate conditions, air and site quality, as well as their interactions. We found that, despite the large environmental gradients encompassed by the forests examined, stand density and age were key drivers of forest growth. We further detected a positive, in some cases non-linear effect of N deposition, most pronounced for beech forests, with a tipping point at ca. 30 kg N ha−1 yr−1. With the exception of a consistent temperature signal on Norway spruce, climate-related predictors and ground-level ozone showed much less generalized relationships with ΔVol. Our results show that, together with the driving forces exerted by stand density and age, N deposition is at least as important as climate to modulate forest growth at continental scale in Europe, with a potential negative effect at sites with high N deposition.
Forfattere
Svein SolbergSammendrag
Det er ikke registrert sammendrag
Sammendrag
A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios.