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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

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Abstract

• Heterobasidion root and butt rot pose a greater risk in continuous cover forestry (CCF) than in rotation forestry (RF) in conifer-dominated forests, regardless of whether selective, gap or shelterwood cutting is used. • Damage from wind, snow, spruce bark beetle, and large pine weevil are likely to be less severe in CCF than in RF. However, the conversion of RF to CCF may briefly expose stands to windthrow. • Browsing by large herbivores on saplings may limit regeneration of tree species other than spruce in continuous cover forestry and reduce tree species diversity, but alternative silvicultural practices may also increase forage availability in the field and shrub layer. Browsing damage outcomes for saplings in CCF are difficult to predict. • For many types of damage in CCF, substantial knowledge gaps complicate the assessment of damage risk.

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Abstract

Plant diseases impair yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, since traditional methods to measure plant diseases are resource intensive and this often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral and thermal imaging, and light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, aeroplanes and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modelling have rarely been integrated. To address this gap, we first review the state-of-the-art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modelling. We discuss how optical sensing can inform epidemiological modelling by improving model selection and parameterisation and providing accurate maps of host plants. Epidemiological modelling can inform optical sensing by boosting measurement accuracy, improving data interpretation and optimising sensor deployment. We consider outstanding challenges in: A) identifying particular diseases; B) data availability, quality and resolution, C) linking optical sensing and epidemiological modelling, and D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose to standardise methods and protocols for optical sensing of plant health and develop open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work.

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Abstract

Naturally regenerated seedlings of Quercus petraea (Mattuschka) Liebl. are often outcompeted by tree species such as Fagus sylvatica L. and Carpinus betulus L., and understorey species like Rubus subg. Rubus. Since plant growth is fundamentally driven by photosynthetic capacity and efficiency, the competitive dynamics between species are influenced by their ability to adapt to varying light conditions through morphological and physiological plasticity. To explore these adaptations, we measured a number of variables indicating growth performance or potential of 60 12-year-old seedlings of Q. petraea, F. sylvatica, and C. betulus as well as individuals of R. subg. Rubus along a gradient of canopy openness and thus radiation. These variables included: a) key leaf traits, including specific leaf area (SLA) and leaf nitrogen (N) content, b) different photosynthesis measurements under constant and fluctuating light, and c) annual shoot length, total height and root collar diameter. Solar radiation was quantified as total site factor (TSF). In all four species, an increase in leaf N content was observed with increasing TSF, which was accompanied by an increase in maximum photosynthetic rate (A ) and growth. However, while this increase was continuous in C. betulus and R. subg. Rubus, a significant increase in A max and growth in Q. petraea and F. sylvatica occurred only in the radiation ranges between 1 % and 20 % and 50–70 % TSF. Measurements of photosynthesis in relation to simulated lightflecks suggest that leaves of Q. petraea are better adapted to prolonged high photosynthetic photon flux density (PPFD) exposure than to fluctuating light. Under these light conditions, especially at TSF levels > 60 %, Q. petraea showed a higher photosynthetic performance than F. sylvatica and C. betulus, in addition to comparable diameter and height growth. To promote Q. petraea regeneration against F. sylvatica and C. betulus competition and reduce necessary vegetation control interventions, we recommend radiation levels > 60 % TSF after the initial establishment phase, when oak seedlings have reached a height of about 0.8 m.

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Abstract

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 physiologicalbased 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.