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.
2017
Authors
Ryan BrightAbstract
No abstract has been registered
Authors
Ryan BrightAbstract
No abstract has been registered
Abstract
Schistidium relictum is described as a new northwest North American and Siberian species of moss. Important distinguishing characters include dull, nearly black plants, with stems densely and evenly foliated, weakly spreading leaves that usually lack awns, and the mostly 1-stratose distal leaf laminae with 2(–3) -stratose margins. The species has a remarkable disjunct distribution pattern with most of the sites where it has been found having been unglaciated during the Pleistocene glaciations. It is restricted to areas with occurrence of calcareous bedrock, especially limestones. It appears to be rather isolated genetically based on molecular studies of total ITS. It is sister to the large clade, ‘Apocarpum’, which consists of species which probably embody its closest known extant relatives.
Abstract
Changes in forest management have been suggested as a government policy to mitigate climate change in Norway. Tree species change is one of the major strategies considered, with the aim to increase the annual uptake of CO2 as well as the long-term storage of carbon (C) in forests. The strategy includes replacing native, deciduous species with fast-growing species, mainly Norway spruce. Forests in western Norway host some of the largest soil C pools in Scandinavia, and may potentially function as a long-term C reservoir as well as a large source of atmospheric CO2 through decomposition. The project BalanC was initiated in 2016 in order to estimate the C storage potential related to tree species in a total of 15 parallel plots of birch and planted Norway spruce at 5 locations in western Norway. In addition to estimates of C stocks in biomass and soils, we investigate soil C processes, soil fungal and earthworm diversity, albedo, and wood product life-cycles. The current presentation focuses on C stocks in soils relative to trees, soil respiration, and soil climate data. Preliminary results indicate that the soil respiration in spruce was 85 % of the respiration in birch, with a span ranging from 55-151%. The preliminary soil temperature and soil moisture data of the spruce stands were 97 and 73%, respectively, of the birch stands, indicating cooler and drier conditions under spruce which may affect decomposition and C accumulation rates. We expect C allocation in the soil to be affected by tree species, with larger C stocks in the forest floor of spruce stands compared to the mineral soil. Consistent differences in the bulk density of soils under each tree species are likely to be observed, pointing out the need to compare soil C stocks based on equal soil mass. The magnitude of the combined C stock in biomass and soil may increase with planting of spruce, however, we also expect an impact on C stability that will affect the overall mitigation effect of this measure.
Abstract
No abstract has been registered
Authors
Ingeborg Callesen Marjo M. Palviainen O. Janne Kjønaas Kȩstutis E. Armolaitis Charlotte RasmussenAbstract
Soil texture is a key soil physical property for soil quality and used in modeling studies through pedotransfer functions (PTF) for the prediction of physical, e.g. hydraulic, soil properties. Soil texture is quantified by a particle size distribution (PSD) of the fine earth fraction and often translated into a texture class using defined separates of clay (0 - 2 µm), silt (2 µm to 20 µm, 50 µm or 63 µm) and sand (20 µm, 50 µm or 63 µm up to 2 mm) illustrated in a texture triangle. Until now pretreatment methods (e.g. humus and carbonate removal and dispersion) followed by standardised sedimentation and sieving methods have been well-defined. From literature and a mini-survey, we know already that laser diffraction is a commonly used analytical method for soil PSD determination in scientific environmental studies that involve soils. A body of literature has documented that colloid-sized fraction results obtained by laser diffraction analysis of fine-textured soil samples are not comparable to those obtained with sedimentation and sieving methods, when translating to the traditional particle size limits clay, silt and sand. Also, operating procedures for pretreatment of soil samples are variable, and the analyzed sample volumes are small, adding to uncertainty. In this study we first compared PSD’s from three different instruments for a set of soil samples to study reproducibility using the analytical operating procedures developed by the owner institutions (Malvern Mastersizer 2000, University of Copenhagen, Coulter LS230, University of Helsinki, and Sympatec Helos, Aarhus University). Secondly, we compared the influence of 1 mm sieving and found decreased fraction standard deviation and improved repeatability of the PSD determination by laser diffraction on the Coulter LS230. 1 mm sieving should be corrected for if the mass is more than a few percent, but depending on study purpose. Thirdly, the laser diffraction PSD’s were compared with PSD’s obtained by sieving and hydrometer analysis showing well-known underestimation of colloids and fine fractions, that increased with colloid content. We conclude that PSD’s obtained by the laser diffraction method are repeatable and mostly reproducible given standardised pretreatment. Translation to texture class using traditional separates does not work well, and more work and new PTF’s for soils are needed that can translate a laser diffraction PSD into a texture class and its associated physical properties for further use in modeling studies.
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Abstract
Small-area estimation is a subject area of growing importance in forest inventories. Modelling the link between a study variable Y and auxiliary variables X— in pursuit of an improved accuracy in estimators—is typically done at the level of a sampling unit. However, for various reasons, it may only be possible to formulate a linking model at the level of an area of interest (AOI). Area-level models and their potential have rarely been explored in forestry. This study demonstrates, with data (Y = stem volume per ha) from four actual inventories aided by aerial laser scanner data (3 cases) or photogrammetric point clouds (1 case), application of three distinct models representing the currency of area-level modelling. The studied AOIs varied in size from forest management units to forest districts, and municipalities. The variance explained by X declined sharply with the average size of an AOI. In comparison with a direct estimate mean of Y in an AOI, all three models achieved practically important reduction in the relative root-mean-squared error of an AOI mean. In terms of the reduction in mean-squared errors, a model with a spatial location effect was overall most attractive. We recommend the pursuit of a spatial model component in area-level modelling as promising within the context of a forest inventory.
Authors
Hans Petersson Johannes Breidenbach David Ellison Sören Holm Anders Muszta Mattias Lundblad Göran R. StåhlAbstract
Many parties to the United Nation's Framework Convention on Climate Change (UNFCCC) base their reporting of change in Land Use, Land-Use Change and Forestry (LULUCF) sector carbon pools on national forest inventories. A strong feature of sample-based inventories is that very detailed measurements can be made at the level of plots. Uncertainty regarding the results stems primarily from the fact that only a sample, and not the entire population, is measured. However, tree biomass on sample plots is not directly measured but rather estimated using regression models based on allometric features such as tree diameter and height. Estimators of model parameters are random variables that exhibit different values depending on which sample is used for estimating model parameters. Although sampling error is strongly influenced by the sample size when the model is applied, modeling error is strongly influenced by the sample size when the model is under development. Thus, there is a trade-off between which sample sizes to use when applying and developing models. This trade-off has not been studied before and is of specific interest for countries developing new national forest inventories and biomass models in the REDD+ context. This study considers a specific sample design and population. This fact should be considered when extrapolating results to other locations and populations.
Authors
Johannes BreidenbachAbstract
No abstract has been registered