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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.

2020

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Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

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The strawberry blossom weevil (SBW), Anthonomus rubi, is a major pest in strawberry fields throughout Europe. Traps baited with aggregation pheromone are used for pest monitoring. However, a more effective lure is needed. For a number of pests, it has been shown that the attractiveness of a pheromone can be enhanced by host plant volatiles. The goal of this study was to explore floral volatile blends of different strawberry species (Fragaria x ananassa and Fragaria vesca) to identify compounds that might be used to improve the attractiveness of existing lures for SBW. Floral emissions of F. x a. varieties Sonata, Beltran, Korona, and of F. vesca, were collected by both solid-phase microextraction (SPME) and dynamic headspace sampling on Tenax. Analysis by gas chromatography/mass spectrometry showed the floral volatiles of F. x ananassa. and F. vesca were dominated by aromatic compounds and terpenoids, with 4-methoxybenzaldehyde (p-anisaldehyde) and α-muurolene the major compounds produced by the two species, respectively. Multi-dimensional scaling analyses separated the blends of the two species and explained differences between F. vesca genotypes and, to some degree, variation between F. x ananassa varieties In two-choice behavioral tests, SBW preferred odors of flowering strawberry plants to those of non-flowering plants, but weevils did not discriminate between odors from F. x ananassa and F. vesca flowering plants. Adding blends of six synthetic flower volatiles to non-flowering plants of both species increased the preference of SBW for these over the plants alone. When added individually to non-flowering plants, none of the components increased the preference of SBW, indicating a synergistic effect. However, SBW responded to 1,4-dimethoxybenzene, a major component of volatiles from F. viridis, previously found to synergize the attractiveness of the SBW aggregation pheromone in field studies.

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The final chapter in the book summarizes the main messages from the preceding chapters. It analyses the diverse views of the bioeconomy concept and supports the view that sustainable bioeconomy development has the potential to change the way we produce and consume natural resources while reducing the negative impacts on the environment. However, there are always risks associated with any new paradigm, hence, it is necessary to ensure transparency in the process, consider the interests of the most vulnerable groups and introduce genuine stakeholder management from the start. Whether, and to what extent, bioeconomy can contribute to the SDGs is a debatable issue. However, several case studies in the book do support the idea that bioeconomy can help in achieving several SDGs. The chapter also highlights the importance of sustainability indicators, including ecological (i.e., the local ecological footprint, total organic carbon, soil nitrogen, transport of minerals from land to rivers and oceans and other ecosystem services), economic and social sustainability indices in the context of bioeconomy development. Their measurement and monitoring are essential to ensure that we are on the sustainable development path. The chapter suggests possible measures to overcome constraints or risks associated with bioeconomy and proposes the necessary conditions required for sustainable bioeconomy development.

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Spring greening has been widely observed across the Northern Hemisphere (NH) using a remotely sensed vegetation index (e.g., the normalized difference vegetation index, NDVI). However, there is still a debate on the ecological effects of spring greening on seasonal carbon and water budgets. This study jointly investigated the concurrent and lagged effects of spring greening on carbon gain (gross primary productivity, GPP) and water loss (evapotranspiration, ET) in the summer-active ecosystems at mid and high latitudes of NH using remote sensing and multimodel ensemble data during 1982–2013. The results showed that the collective promotion of spring greening to concurrent GPP and ET is widespread despite variations in magnitude and significance. Both beneficial and adverse lagged effects of spring greening on summer GPP commonly appear with an obvious spatial heterogeneity and difference among climate-plant types. However, the expected significant suppression of spring greening to summer GPP was rarely observed even in the areas where spring ET was significantly promoted by spring greening. Nevertheless, when drought was taken into account, the response patterns of spring water use to spring greening varied to some extent, and the adverse lagged effect of spring greening to summer GPP appeared or strengthened in some regions, especially during the years with dry summer. Given the predicted warming of the climate and more frequent climatic extremes, the adverse effect of spring greening should be given more attention.

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In studies of consumption of local food specialties (LFSs), individual personalities are rarely mentioned. In this article, we want to expand on and provide a nuanced explanation of the characteristics of these consumers of these products, asking: Are there any personality traits that characterize these consumers? We use the Big Five personality model to unpack the relationship between individuals' personalities and choices of LFS in the Norwegian context. The model consists of the following five personal traits: extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. These personality traits are latent, but through questions regarding behavior, the traits may be revealed. To construct latent variables to measure these traits, we apply the graded response model. Furthermore, socioeconomic variables are combined with personality traits in logistic regression models to find the relationships between personality and choice of Norwegian LFSs. Our results show that in all models the latent variable Openness to experience was one of the most important predictors of all the choices of LFS made by individuals. Openness to experience is characterized by fantasy, aesthetic sensitivity, attentiveness to inner feelings, preference for variety, and intellectual curiosity. The consequence of the connection between Openness to experience and LFS is that stakeholders may take this into account when seeking to increase sales.

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There has been much recent research interest in the existence of a major axis of life‐history variation along a fast–slow continuum within almost all major taxonomic groups. Eco‐evolutionary models of density‐dependent selection provide a general explanation for such observations of interspecific variation in the "pace of life." One issue, however, is that some large‐bodied long‐lived “slow” species (e.g., trees and large fish) often show an explosive “fast” type of reproduction with many small offspring, and species with “fast” adult life stages can have comparatively “slow” offspring life stages (e.g., mayflies). We attempt to explain such life‐history evolution using the same eco‐evolutionary modeling approach but with two life stages, separating adult reproductive strategies from offspring survival strategies. When the population dynamics in the two life stages are closely linked and affect each other, density‐dependent selection occurs in parallel on both reproduction and survival, producing the usual one‐dimensional fast–slow continuum (e.g., houseflies to blue whales). However, strong density dependence at either the adult reproduction or offspring survival life stage creates quasi‐independent population dynamics, allowing fast‐type reproduction alongside slow‐type survival (e.g., trees and large fish), or the perhaps rarer slow‐type reproduction alongside fast‐type survival (e.g., mayflies—short‐lived adults producing few long‐lived offspring). Therefore, most types of species life histories in nature can potentially be explained via the eco‐evolutionary consequences of density‐dependent selection given the possible separation of demographic effects at different life stages.

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Rations with low to negative dietary cation-anion difference (DCAD) given to dairy cows before calving reduce the risk of hypocalcaemia (milk fever). Different strategies for increasing forage DCAD were investigated in field trials in Central and Western Norway. Fertilisation with 70, 140 or 210 kg Cl per hectare as calcium chloride and low supply rates of K reduced DCAD in forage harvested at late developmental stages in spring growth of timothy and mead-ow fescue. The ideal negative DCAD was only attained on soils very low in plant available K. Timing (spring versus late spring) and source of Cl (CaCl2 versus MgCl2) were of no importance for the result. When pure stands of seven grasses were fertilised in spring either without chloride or with 140 kg chloride per hectare, the lowest values of DCAD after chloride fertilisation were found in perennial ryegrass and reed canary grass. By comparison, cocksfoot had equally high or higher Cl concentrations in its tissues, but accumulated more K, and seemed to be poorly suit-ed for low DCAD forage production. It was concluded that Cl fertilisation is a more efficient means of controlling DCAD than sward species composition.