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.
2025
Authors
Kjersti Holt HanssenAbstract
No abstract has been registered
Authors
Simone Bianchi Andreas Brunner Kjersti Holt Hanssen Hannu Hökkä Urban Nilsson Nils Fahlvik Jari HynynenAbstract
• There is still a lack of knowledge on growth and yield (G&Y) in continuous cover forestry (CCF). Most published studies are on the selection system with Norway spruce. • Published comparisons of the selection system with rotation forestry (RF) show contrasting results. Generally, there seems to be a trend toward faster stand growth in RF. • However, there are many uncertainties due to several confounding factors, such as stand-density effects, site-quality classifcation, and/or growth models used. Most studies do not properly account for all these factors, making it diffcult to generalise their results. • The optimal stand density trade off for the selection system between stand growth and recruitment should be better investigated. Preliminary results show this could strongly affect stand growth. • There is even less knowledge related to G&Y during conversion, a potential bottleneck for full implementation of CCF in the region.
Authors
Susanne Suvanto Adriane Esquivel-Muelbert Mart-Jan Schelhaas Julen Astigarraga Rasmus Astrup Emil Cienciala Jonas Fridman Helena M. Henttonen Georges Kunstler Gerald Kändler Louis A. König Paloma Ruiz-Benito Cornelius Senf Golo Stadelmann Ajdin Starcevic Andrzej Talarczyk Miguel A. Zavala Thomas A. M. PughAbstract
No abstract has been registered
Authors
G. Brundu S. Follak J. Pergl D. Chapman E. Branquart S. Buholzer Inger Sundheim Fløistad G. Fried M. Herbst E. Marchante Valkenburg van Valkenburg R. TannerAbstract
No abstract has been registered
Abstract
Long-term monitoring of ecosystems is the only direct method to provide insights into the system dynamics on a range of timescales from the temporal resolution to the duration of the record. Time series of typical environmental variables reveal a striking diversity of trends, periodicities, and long-range correlations. Using several decades of observations of water chemistry in first-order streams of three adjacent catchments in the Harz mountains in Germany as example, we calculate metrics for these time series based on ordinal pattern statistics, e.g. permutation entropy and complexity, Fisher information, or q-complexity, and other indicators like Tarnopolski diagrams. The results are compared to those obtained for reference statistical processes, like fractional Brownian motion or ß noise. After detrending and removing significant periodicities from the time series, the distances of the residuals to the reference processes in this space of metrics serves as a classification of nonlinear dynamical behavior, and to judge whether inter-variable or rather inter-site differences are dominant. The classification can be combined with knowledge about the processes driving hydrochemistry, elucidating the connections between the variables. This can be the starting point for the next step, constructing causal networks from the multivariate dataset.
Abstract
Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments.
Abstract
No abstract has been registered
Authors
Lorène J. Marchand Jožica Gričar Paolo Zuccarini Inge Dox Bertold Mariën Melanie Verlinden Thilo Heinecke Peter Prislan Guillaume Marie Holger Lange Jan Van den Bulcke Josep Penuelas Patrick Fonti Matteo CampioliAbstract
No abstract has been registered
Authors
Klaus Steenberg Larsen Carl-Fredrik Johannesson Jenni Nordén Holger Lange Hanna Marika SilvennoinenAbstract
Non-steady-state chambers are widely used for measuring the exchange of greenhouse gases (GHGs) between soils or ecosystems and the atmosphere. It is known that non-steady-state chambers induce a non-linear concentration development inside the chamber after closure, even across short chamber closure periods, and that both linear and non-linear flux estimates are impacted by the chamber closure period itself. However, despite the existence of recommendations on how long to keep the chamber closed, it has been little explored to what extent the length of the chamber closure period affects the estimated flux rates, and which closure periods may provide the most accurate linear and non-linear flux estimates. In the current study, we analyzed how linear regression and Hutchinson and Mosier (1981) modeled flux estimates were affected by the length of the chamber closure period by increasing it in increments of 30 s, with a minimum and maximum chamber closure period of 60 and 300 s, respectively. Across 3,159 individual soil CO2 and CH4 flux measurements, the effect of chamber closure period length varied between 1.4–8.0% for linear regression estimates and between 0.4–17.8% for Hutchinson–Mosier estimates and the largest effect sizes were observed when the measured fluxes were high. Both linear regression and Hutchinson–Mosier based flux estimates decreased as the chamber closure period increased. This effect has been observed previously when using linear regression models, but the observed effect on Hutchinson-Mosier modeled estimates is a novel finding. We observed a clear convergence between the short-period linear regression estimates and the long-period Hutchinson–Mosier estimates, showing that closure periods as short as possible should be used for linear regression flux estimation, while ensuring long-enough closure periods to observe a stabilization of flux estimates over time when using the Hutchinson-Mosier model. Our analysis was based on soil flux measurements, but because the perturbation of the concentration gradient is related to the non-steady-state chamber technique rather than the measured ecosystem component, our results have implications for all flux measurements conducted with non-steady-state chambers. However, optimal chamber closure times may depend on individual chamber designs and analyzer setups, which suggests testing individual chamber/system designs for optimal measurement periods prior to field application
Authors
Holger LangeAbstract
No abstract has been registered