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.
2020
Abstract
No abstract has been registered
Authors
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 VriesAbstract
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.
Abstract
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.
Authors
Hanne Kathrine Sjølie Clara Antón Fernández Luiz Goulart Jogeir N. Stokland Gregory S. Latta Birger SolbergAbstract
No abstract has been registered
Abstract
An understanding of the relationship between volume increment and stand density (basal area, stand density index, etc.) is of utmost importance for properly managing stand density to achieve specific management objectives. There are two main approaches to analyse growth–density relationships. The first relates volume increment to stand density through a basic relationship, which can vary with site productivity, age, and potentially incorporates treatment effects. The second is to relate the volume increment and density of thinned experimental plots relative to that of an unthinned experimental plot on the same site. Using a dataset of 229 thinned and unthinned experimental plots of Norway spruce, a growth model is developed describing the relationship between gross or net volume increment and basal area. The models indicate that gross volume increases with increasing basal area up to 50 m2 and thereafter becomes constant out to the maximum basal area. Alternatively, net volume increment was maximized at a basal area of 43 m2 and decreased with further increases in basal area. However, the models indicated a wide range where net volume increment was essentially constant, varying by less than 1 m3 ha−1 year−1. An analysis of different thinning scenarios indicated that the relative relationship between volume increment and stand density was dynamic and changed over the course of a rotation.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.
Abstract
No abstract has been registered