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.
2025
Authors
Zahra Bitarafan Wiktoria Kaczmarek-Derda Therese With Berge Inger Sundheim Fløistad Christian AndreasenAbstract
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Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A GilliganAbstract
No abstract has been registered
Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A. GilliganAbstract
Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.
Authors
Komi Mensah Agboka Elfatih M. Abdel-Rahman Daisy Salifu Brian Kanji Frank T. Ndjomatchoua Ritter Atoundem Guimapi Sunday Ekesi Landmann TobiasAbstract
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.
Abstract
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Authors
Anne MuolaAbstract
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Authors
Ivan M. De-la-Cruz Femke Batsleer Dries Bonte Carolina Diller Timo Hytönen José Luis Izquierdo Sonia Osorio David Posé Aurora de la Rosa Martijn Lodewijk Vandegehuchte Anne Muola Johan A. StenbergAbstract
Background and Aims Climate change is causing increasing temperatures and drought, creating new environmental conditions, which species must cope with. Plant species can respond to these shifting environments by escaping to more favorable environments, undergoing adaptive evolution, or exhibiting phenotypic plasticity. In this study, we investigate genotype responses to variation in environmental conditions (genotype-by-environment interactions; G × E) over multiple years to gain insights into the plasticity and potential adaptive responses of plants to environmental changes in the face of climate change. Methods We reciprocally transplanted 16 European genotypes of Fragaria vesca (Rosaceae), the woodland strawberry, between four sites along a latitudinal gradient from 40°N (Spain) to 70°N (northern Finland). We examined G × E interactions in plant performance traits (fruit and stolon production and rosette size) under ambient weather conditions and a reduced precipitation treatment (as a proxy for drought), at these sites over two years. Key Results Our findings reveal signals of local adaptation for fruit production at the latitudinal extremes of F. vesca distribution. No clear signals of local adaptation for stolon production were detected. Genotypes from higher European latitudes were generally smaller than genotypes from lower latitudes across almost all sites, years and both treatments, indicating a strong genetic control of plant size in these genotypes. We found mixed responses to reduced precipitation: while several genotypes exhibited poorer performance under the reduced precipitation treatment across most sites and years, with the effect being most pronounced at the driest site, other genotypes responded to reduced precipitation by increasing fruit and/or stolon production and/or growing larger across most sites and years, particularly at the wettest site. Conclusions This study provides insights into the influence of different environments on plant performance at a continental scale. While woodland strawberry seems locally adapted in more extreme environments, reduced precipitation results in winners and losers among its genotypes. This may ultimately reduce genetic variation in the face of increasing drought frequency and severity, with implications for the species’ capacity to adapt.
Abstract
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.
Authors
Carey Donald Gunnhild Jaastad M.H. Flaigeng Sylvain Alain Yves Merel Josef Rasinger Marc HG Berntssen Ikram BelghitAbstract
No abstract has been registered