Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2021
Forfattere
Matthias Vanmaercke Panos Panagos Tom Vanwalleghem Antonio Hayas Saskia Foerster Pasquale Borrelli Mauro Rossi Dino Torri Javier Casali Lorenzo Borselli Olga Vigiak Michael Maerker Nigussie Haregeweyn Sofie De Geeter Wojciech Zglobicki Charles Bielders Artemi Cerdà Christian Conoscenti Tomas de Figueiredo Bob Evans Valentin Golosov Ion Ionita Christos Karydas Adam Kertesz Josef Krasa Caroline Le Bouteiller Maria Radoane Ratko Ristic Svetla Rousseva Milos Stankoviansky Jannes Stolte Christian Stolz Rebecca Bartley Scott Wilkinson Ben Jarihani Jean PoesenSammendrag
Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Marit Skuterud VennatrøSammendrag
Formålet med kartleggingsprogrammet «Skadegjørere i potet» er å få kunnskap om status med hensyn til forekomst av planteskadegjørerne lys ringråte (Clavibacter michigaensis spp.), mørk ringråte (Rastonia solancearum), rotgallnematodene Meloidogyne chitwoodi og M. fallax samt potetkreft (Synchytrium endobioticum) i norsk produksjon av mat- og industripotet. Denne rapporten omhandler status for rotgallnematodene. Rotgallnematoder (Meloidogyne spp.) er en stor gruppe obligate planteparasittære som finnes over hele verden. Skadene etter rotgallnematoder forringer både kvalitet og avling, og gir store avlingstap på verdensbasis. M. chitwoodi og M. fallax har mange vertsplanter, og er vanskelige å bekjempe dersom de etablerer seg. Derfor ansees disse artene som alvorlige planteskadegjørere, og som en trussel mot europeisk potet og gulrot produksjon. Både M. chitwoodi og M. fallax er påvist i Europa i begrenset omfang. Begge artene de senere årene funnet i Sverige. M. chitwoodi og M. fallax er ikke påvist i Norge, men det er risiko for at begge artene kan etablere seg og gjøre omfattende skade i norsk potet- og gulrotproduksjon….
Forfattere
Tonje Økland Siri Lie Olsen Rune HalvorsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Markus A. K. Sydenham Zander Venter Trond Reitan Claus Rasmussen Astrid Brekke Skrindo Daniel Skoog Kaj-Andreas Hanevik Stein Joar Hegland Yoko Dupont Anders Nielsen Joseph Chipperfield Graciela Monica RuschSammendrag
1. Predicting plant-pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant-pollinator interactions and of the species richness, diversity, and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee-flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee-flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild, and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models’ ability to predict pairwise plant-bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee-flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48), and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant-pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function.
Forfattere
Markus A. K. Sydenham Zander Venter Trond Reitan Claus Rasmussen Astrid Brekke Skrindo Daniel Ingvar Jeuderan Skoog Kaj-Andreas Hanevik Stein Joar Hegland Yoko L. Dupont Anders Nielsen Joseph Chipperfield Graciela RuschSammendrag
1. Predicting plant–pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant–pollinator interactions and of the species richness, diversity and abundance of pollinators visiting flowers. We call the framework ‘MetaComNet’ because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee–flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee–flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250 m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant–bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee–flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48) and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant–pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function. interactions, network, plants, pollinators, predict, random forest
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
The ascomycete Hymenoscyphus fraxineus has spread across most of the host range of European ash with a high level of mortality, causing important economic, cultural and environmental effects. We present a novel method combining a Monte-Carlo approach with a generalised additive model that confirms the importance of meteorology to the magnitude and timing of H. fraxineus spore emissions. The variability in model selection and the relative degree to which our models are over- or under-fitting the data has been quantified. We find that both the daily magnitude and timing of spore emissions are affected by meteorology during and prior to the spore emission diurnal peak. We found the daily emission magnitude has the strongest associations to weekly average net radiation and leaf moisture before the emission, soil temperature during the day before emission and net radiation during the spore emission. The timing of the daily peak in spore emissions has the strongest associations to net radiation both during spore emission and in the day preceding the emission. The seasonal peak in spore emissions has a near-exponential increase/decrease, and the mean daily emission peak is approximately Gaussian.