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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.

2025

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Low pollinator richness and abundance is a primary driver of pollination deficits and may lead to reduced yields (production deficits). In response, domesticated honeybees are often used to increase pollination success, even though honeybees are less efficient pollinators than naturally occurring wild bees. Here, we explored whether Norwegian apple orchards experience pollination and production deficits, and if such deficits could be related to specific pollinator groups and activity. We conducted a supplemental pollination experiment and measured seed set and yield (fruit set x weight) for three cultivars, in six orchards, in two distinct apple growing regions in central Norway, for two years. In addition, we used cameras to record relative pollinator activity throughout the flowering period. Overall, we found a pollination and production deficit across all cultivars, although there were differences in pollination deficit among cultivars. Three orchards had a pollination deficit both years of the study, suggesting sub-optimal orchard structure and/or a lack of pollinators. However, we found that solitary bees significantly reduced both pollination and production deficit, suggesting that orchard management actions should focus on increasing wild bee diversity and abundance.

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This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector- borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications. • The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics. • Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models. • The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.

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Land-use changes threaten ecosystems and are a major driver of species loss. Plants may adapt or migrate to resist global change, but this can lag behind rapid anthropogenic changes to the environment. Our data show that natural modulations of the microbiome of grassland plants in response to experimental land-use change in a common garden directly affect plant phenotype and performance, thus increasing plant tolerance. In contrast, direct effects of fertilizer application and mowing on plant phenotypes were less strong. Land-use intensity-specific microbiomes caused clearly distinguishable plant phenotypes also in a laboratory experiment using gnotobiotic strawberry plants in absence of environmental variation. Therefore, natural modulations of the plant microbiome may be key to species persistence and ecosystem stability. We argue that a prerequisite for this microbiome-mediated tolerance is the availability of diverse local sources of microorganisms facilitating rapid modulations in response to change. Thus, conservation efforts must protect microbial diversity, which can help mitigate the effects of global change and facilitate environmental and human health.

2024