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.
2019
Authors
Jürgen Dengler Thomas J. Matthews Manuel J. Steinbauer Sebastian Wolfrum Steffen Boch Alessandro Chiarucci Timo Conradi Iwona Dembicz Corrado Marcenó Itziar García-Mijangos Arkadiusz Nowak David Storch Werner Ulrich Juan Antonio Campos Laura Cancellieri Marta Carboni Giampiero Ciaschetti Pieter De Frenne Jiří Doležal Christian Dolnik Franz Essl Edy Fantinato Goffredo Filibeck John-Arvid Grytnes Riccardo Guarino Behlül Güler Monika Janišová Ewelina Klichowska Łukasz Kozub Anna Kuzemko Michael Manthey Anne Mimet Alireza Naqinezhad Christian Pedersen Robert K. Peet Vincent Pellissier Remigiusz Pielech Giovanna Potenza Leonardo Rosati Massimo Terzi Orsolya Valkó Denys Vynokurov Hannah White Manuela Winkler Idoia BiurrunAbstract
Aim Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non‐forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm2 to 1,024 m2. Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types. Results The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law. Main conclusions We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.
Authors
Eric Post Eva Beyen Pernille Sporon Bøving R. Conor Higgins Christian John Jeff Kerby Christian Pedersen David A. WattsAbstract
We report an observation of a flightless fledgling Lapland longspur (Calcarius lapponicus (Linnaeus, 1758)) at a long-term study site near Kangerlussuaq, Greenland, in late July 2018. Based on our observations of longspur nests at the site dating back to 1993, we estimate that the fledgling observed in 2018 may have originated from a nest initiated 12–37 d later than nesting in previous years. Onset of spring in 2018 was late, but comparable with other years in which longspur nests were observed a full calendar month earlier than in 2018. An analysis including multiple candidate predictor variables revealed a strong negative association between estimated longspur nest initiation dates and mean May temperature, as well as a weaker association with the length of the annual period of vegetation green up at the site. Given the limitations of our data, however, we are unable to assign causality to the 2018 observation, and cannot rule out other possibilities, such as that it may have resulted from a second clutch.
Abstract
Aim: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: Mainland Norway, covering ca. 324,000 km2. Methods: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.
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Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field.
Abstract
No abstract has been registered
Abstract
We investigated the impact of Norway’s current zonal carnivore management system for four large carnivore species on sheep farming. Sheep losses increased when the large carnivores were reintroduced, but has declined again after the introduction of the zoning management system. The total number of sheep increased outside, but declined slightly inside the management zones. The total sheep production increased, but sheep farming was still lost as a source of income for many farmers. The use of the grazing resources became more extensive. Losses decreased because sheep were removed from the open outfield pastures and many farmers gave up sheep farming. While wolves expel sheep farming from the outfield grazing areas, small herds can still be kept in fenced enclosures. Bears are in every respect incompatible with sheep farming. Farmers adjust to the seasonal and more predictable behavior of lynx and wolverine, although these species also may cause serious losses when present. The mitigating efforts are costly and lead to reduced animal welfare and lower income for the farmers, although farmers in peri-urban areas increasingly are keeping sheep as an avocation. There is a spillover effect of the zoning strategy in the sense that there is substantial loss of livestock to carnivores outside, but geographically near the management zones. The carnivore management policy used in Norway is a reasonably successful management strategy when the goal is to separate livestock from carnivores and decrease the losses, but the burdens are unequally distributed and farmers inside the management zones are at an economic disadvantage.
Authors
Idoia Biurrun Sabina Burrascano Iwona Dembicz Riccardo Guarino Jutta Kapfer Remigiusz Pielech Itziar García-Mijangos Viktoria Wagner Salza Palpurina Anne Mimet Vincent Pellissier Corrado Marcenó Arkadiusz Nowak Ariel Bergamini Steffen Boch Anna Mária Csergő John-Arvid Grytnes Juan Antonio Campos Brigitta Erschbamer Borja Jiménez-Alfaro Zygmunt Kącki Anna Kuzemko Michael Manthey Koenraad Van Meerbeek Grzegorz Swacha Elias Afif Juha M. Alatalo M Aleffi Manuel Babbi Zoltán Bátori Elena Belonovskaya Christian Berg Kuber Prasad Bhatta Laura Cancellieri Tobias Ceulemans Balázs Deák László Demeter Lei Deng Jiří Doležal Christian Dolnik Wenche Dramstad Pavel Dřevojan Klaus Ecker Franz Essl J. Etzold Goffredo Filibeck Wendy Fjellstad Behlul Güler Michal Hájek Daniel Hepenstrick John G. Hodgson João Honrado Annika Jagerbrand Monika Janišová Philippe Jeanneret András Kelemen Philipp Kirschner Ewelina Klichowska Ganna Kolomiiets Łukasz Kozub Jan Lepš Regina Lindborg Swantje Löbel Angela Lomba Martin Magnes Helmut Mayrhofer Marek Malicki Ermin Mašić Eliane S. Meier Denis Mirin Ulf Molau Ivan Y. Moysiyenko Alireza Naqinezhad Josep M. Ninot M Nobis Christian Pedersen Aaron Pérez-Haase Jan Peters Eulàlia Pladevall-Izard Jan Rolecek Vladimir Ronkin Galina Savchenko Dariia Shyriaieva Hanne Sickel Carly Stevens Sebastian Świerszcz Csaba Tölgyesi Nadezda Tsarevskaya Orsolya Valkó Carmen Van Mechelen Iuliia Vashenyak Ole Reidar Vetaas Denys Vynokurov Emelie Waldén Stefan Widmer Sebastian Wolfrum Anna Wróbel Ekaterina Zlotnikova Jürgen DenglerAbstract
Abstract: GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). Following a previous Long Database Report (Dengler et al. 2018, Phyto-coenologia 48, 331–347), we provide here the first update on content and functionality of GrassPlot. The current version (GrassPlot v. 2.00) contains a total of 190,673 plots of different grain sizes across 28,171 independent plots, with 4,654 nested-plot series including at least four grain sizes. The database has improved its content as well as its functionality, including addition and harmonization of header data (land use, information on nestedness, structure and ecology) and preparation of species composition data. Currently, GrassPlot data are intensively used for broad-scale analyses of different aspects of alpha and beta diversity in grassland ecosystems.
Authors
Xiao Huang Mats Höglind Knut Bjørkelo Torben Christensen Kjetil Fadnes Teresa Gómez de la Bárcena Åsa Kasimir Leif Klemedtsson Bjørn Kløve Anders Lyngstad Mikhail Mastepanov Hannu Marttila Marcel Van Oijen Peter Petros Ina Pohle Jagadeesh Yeluripati Hanna Marika SilvennoinenAbstract
Cultivated organic soils (7-8% of Norway’s agricultural land area) are economically important sources for forage production in some regions in Norway, but they are also ‘hot spots’ for greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR; funded by the Research Council of Norway, decision no. 281109) will evaluate how water table management and the intensity of other management practices (i.e. tillage and fertilization intensity) affects both GHG emissions and forage’s quality & production. The overall aim of MYR is to generate useful information for recommendations on climate-friendly management of Norwegian peatlands for both policy makers and farmers. For this project, we established two experimental sites on Norwegian peatlands for grass cultivation, of which one in Northern (subarctic, continental climate) and another in Southern (temperate, coastal climate) Norway. Both sites have a water table level (WTL) gradient ranging from low to high. In order to explore the effects of management practices, controlled trials with different fertilization strategies and tillage intensity will be conducted at these sites with WTL gradients considered. Meanwhile, GHG emissions (including carbon dioxide, methane and nitrous oxide), crop-related observations (e.g. phenology, production), and hydrological conditions (e.g. soil moisture, WTL dynamics) will be monitored with high spatiotemporal resolution along the WTL gradients during 2019-2021. Besides, MYR aims at predicting potential GHG mitigation under different scenarios by using state-of-the-art modelling techniques. Four models (BASGRA, Coup, DNDC and ECOSSE), with strengths in predicting grass growth, hydrological processes, soil nitrification-denitrification and carbon decomposition, respectively, will be further developed according to the soil properties. Then these models will be used independently to simulate biogeochemical and agroecological processes in our experimental fields. Robust parameterization schemes will be based on the observational data for both soil and crop combinations. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. We will couple these process-based models with optimization algorithm to explore the potential reduction in GHG emissions with consideration of production sustenance, and upscale our assessment to regional level.