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
Valentina Gallina Silvia Torresan Alex Zabeo Jonathan Rizzi Sandro Carniel Mauro Sclavo Lisa Pizzol Antonio Marcomini Andrea CrittoAbstract
Coastal erosion is an issue of major concern for coastal managers and is expected to increase in magnitude and severity due to global climate change. This paper analyzes the potential consequences of climate change on coastal erosion (e.g., impacts on beaches, wetlands and protected areas) by applying a Regional Risk Assessment (RRA) methodology to the North Adriatic (NA) coast of Italy. The approach employs hazard scenarios from a multi-model chain in order to project the spatial and temporal patterns of relevant coastal erosion stressors (i.e., increases in mean sea-level, changes in wave height and variations in the sediment mobility at the sea bottom) under the A1B climate change scenario. Site-specific environmental and socio-economic indicators (e.g., vegetation cover, geomorphology, population) and hazard metrics are then aggregated by means of Multi-Criteria Decision Analysis (MCDA) with the aim to provide an example of exposure, susceptibility, risk and damage maps for the NA region. Among seasonal exposure maps winter and autumn depict the worse situation in 2070–2100, and locally around the Po river delta. Risk maps highlight that the receptors at higher risk are beaches, wetlands and river mouths. The work presents the results of the RRA tested in the NA region, discussing how spatial risk mapping can be used to establish relative priorities for intervention, to identify hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies.
Authors
Jonathan RizziAbstract
No abstract has been registered
Authors
Juraj Parajka Nejc Bezak John Burkhart Bjarki Hauksson Ladislav Holko Yeshewa Hundecha Michal Jenicek Pavel Krajčí Walter Mangini Peter Molnar Philippe Riboust Jonathan Rizzi Aynur Sensoy Guillaume Thirel Alberto ViglioneAbstract
This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000–2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.
Abstract
No abstract has been registered
Authors
Finn-Arne HaugenAbstract
No abstract has been registered
Abstract
The extent of land lease is increasing in many countries, including Norway. This paper develops a von Thünen type model of optimal land plots to lease from a farm’s center. For a single farm setting, the optimality principle is that land is leased as long as the expected marginal value of leasing a tract of land is greater than or equal to the expected marginal costs of leasing the land. The single farm model setting captures land lease at the extensive margin, i.e., under absence of competition for leasing land. Land lease at the intensive margin, i.e., when there is competition for leasing farm fields, is more interesting. We distinguish between two cases. In the first case, continued farm operations do not depend on being able to lease more land. Then we show that optimal land lease results when the expected profits for each farm of leasing its least profitable field is equal among farms competing for the same farm field. This also corresponds to an economically efficient allocation of leased land. Our second case at the intensive margin is more complicated. Here, farm survival depends on attracting acreage of leased land to allow for investment in cost saving technology. We show that the resulting allocation of leased land corresponds to the solution of a game involving bidding for land in order to prevent other farmers from getting land, which in turn leads to farmer exit and therefore increases the future supply of land available at the land lease market. In the first round of the game, winners of the land lease auction pay more for the leased land than they would have done in the absence of preventive bidding. The model framework is applicable for other settings where locking out competitors are parts of agents’ strategy space.
Authors
Christian PedersenAbstract
No abstract has been registered
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.