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.
2021
Abstract
No abstract has been registered
Authors
Tommi Nyman Elena Papadopoulou Eeva Ylinen Saskia Wutke Craig T. Michell Ludmila Sromek Tuula Sinisalo Elena Andrievskaya Vyacheslav Alexeev Mervi KunnasrantaAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
We designed and deployed an apparatus to apply UV light for suppression of powdery mildew in open field production of strawberry. The unit was evaluated in a commercial production field for one season, and for two additional seasons in open field research plots at the University of Florida Gulf Coast Research and Education Center. The apparatus contained two 180-cm-long hemicylindrical arrays of twenty 55-W low-pressure discharge UV-C lamps (operated at 30 W; peak wavelength = 254 nm) backed by polished aluminum reflectors covering two adjacent beds of the strawberry planting. The lamp arrays were suspended within a steel carriage that was tractor-drawn through the planting at 2.3, 4.6, and 5.6 km h−1. Nighttime applications of UV-C at doses ranging from 65 to 170 J⋅m−2 either once or twice weekly provided suppression of foliar and fruit disease that was consistently equal to or better than that provided by a commercial calendar-based fungicide spray program.
Authors
Sigridur DalmannsdottirAbstract
No abstract has been registered
Authors
Sigridur DalmannsdottirAbstract
No abstract has been registered
Conference lecture – The role of inducible defenses in bark beetle-conifer interactions
Paal Krokene
Authors
Paal KrokeneAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
Pollination sustains biodiversity and food security, but pollinators are threatened by habitat degradation, fragmentation, and loss. We assessed how remaining forest influenced bee visits to flowers in an oil palm-dominated landscape in Borneo. We observed bee visits to six plant species: four crops (Capsicum frutescens L. “chili”; Citrullus lanatus (Thunb.) Matsum & Nakai “watermelon”; Solanum lycopersicum L. “tomato”; and Solanum melongena L. “eggplant”); one native plant Melastoma malabathricum L. “melastome”; and the exotic Turnera subulata Smith “turnera”. We made one local grid-based and one landscape-scale transect-based study spanning 208 and 2130 m from forest, respectively. We recorded 1249 bee visits to 4831 flowers in 1046 ten-min observation periods. Visit frequency varied among plant species, ranging from 0 observed visits to S. lycopersicum to a mean of 0.62 visits per flower per 10 min to C. lanatus. Bee visitation frequency declined with distance from forest in both studies, with expected visitation frequency decreasing by 55% and 66% at the maximum distance from forest in each study. We also tested whether the distance to the nearest oil palm patch, with a maximum distance of 144 m, influenced visitation, but found no such associations. Expected visitation frequency was 70%–77% lower for plants close to a 200 ha forest fragment compared with those near large continuous forests (>400 ha). Our results suggest that, although found throughout the oil palm-dominated landscape, bees depend on remaining forests. Larger forests support more bees, though even a 50 ha fragment has a positive contribution. Abstract in Indonesian is available with online material.
Abstract
Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.