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.
2013
Abstract
Information on tree species effects on soil organic carbon (SOC) stocks is scattered and there have been few attempts to synthesize results for forest floor and mineral soil C pools. We reviewed and synthesized current knowledge of tree species effects on SOC stocks in temperate and boreal forests based on common garden, retrospective paired stand and retrospective single-tree studies. There was evidence of consistent tree species effects on SOC stocks. Effects were clearest for forest floor C stocks (23 of 24 studies) with consistent differences for tree genera common to European and North American temperate and boreal forests. Support for generalization of tree species effects on mineral soil C stocks was more limited, but significant effects were found in 13 of 22 studies that measured mineral soil C. Proportional differences in forest floor and mineral soil C stocks among tree species suggested that C stocks can be increased by 200–500% in forest floors and by 40–50% in top mineral soil by tree species change. However, these proportional differences within forest floors and mineral soils are not always additive: the C distribution between forest floor and mineral soil rather than total C stock tends to differ among tree species within temperate forests. This suggests that some species may be better engineers for sequestration of C in stable form in the mineral soil, but it is unclear whether the key mechanism is root litter input or macrofauna activity. Tree species effects on SOC in targeted experiments were most consistent with results from large-scale inventories for forest floor C stocks whereas mineral soil C stocks appeared to be stronger influenced by soil type or climate than by tree species at regional or national scales. Although little studied, there are indications that higher tree species diversity could lead to higher SOC stocks but the role of tree species diversity per se vs. species identity effects needs to be disentangled in rigorous experimental designs. For targeted use of tree species to sequester soil C we must identify the processes related to C input and output, particularly belowground, that control SOC stock differences. We should also study forms and stability of C along with bulk C stocks to assess whether certain broadleaves store C in more stable form. Joint cooperation is needed to support syntheses and process-oriented work on tree species and SOC, e.g. through an international network of common garden experiments.
Abstract
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Authors
Synnøve RivedalAbstract
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Abstract
Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m2. TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen’s Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models.
Authors
Belachew Asalf TadesseAbstract
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Abstract
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Authors
Christian PedersenAbstract
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Authors
Trygve S. Aamlid Siri Fjellheim Abdelhameed Elameen Sonja Klemsdal Kristin Daugstad Hans Martin Hanslin Knut Hovstad Dagmar Hagen Knut Rydgren Line RosefAbstract
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Authors
John A. Finn Laura Kirwan John Connolly Maria Teresa Sebastia Aslaug Helgadottir Ole Hans Baadshaug Gilles Bélanger Alistair Black Caroline Brophy Rosemary P. Collins Jure Cop Sigridur Dalmannsdottir Ignacio Delgado Anjo Elgersma Michael Fothergill Bodil Frankow-Lindberg An Ghesquiere Barbara Golinska Piotr Golinski Philippe Grieu Anne-Maj Gustavsson Mats Höglind Olivier Huguenin-Elie Marit Jørgensen Zydre Kadziuliene Päivi Kurki Rosa Llurba Tor Lunnan Claudio Porqueddu Matthias Suter Ulrich Thumm Andreas LüscherAbstract
No abstract has been registered
Abstract
No abstract has been registered