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.
2026
Abstract
Algal-based wastewater treatment (phycoremediation) relies on microbial interactions within the algal phycosphere that are associated with algal growth and nutrient removal. However, the temporal dynamics of these communities under operational conditions remain poorly resolved. Using 16S and 18S rRNA gene metabarcoding, this study characterized bacterial and eukaryotic communities across alga-attached and free-living size fractions and over time in a raceway-based, pilot-scale phycoremediation system using a filamentous algal co-culture under semicontinuous municipal wastewater flow. Bacterial community composition in the phycosphere overlapped substantially with that observed in a previous laboratory-scale study using the same algal co-culture, with many highly abundant ASVs shared across studies, supporting consistency of key community members across scales. Phycosphere community dynamics were temporally aligned with algal growth, with bacterial alpha diversity in the alga-attached fraction highest during periods of active algal growth and declining with the onset of algal phosphorus limitation. During this high-diversity phase, several orders within Alphaproteobacteria were enriched, followed by declines as the algal culture progressed toward reduced growth. Eukaryotic communities also showed clear successional trends, with Perkinsids (Alveolata) increasing during peak algal biomass before giving way to diverse protists and rotifers. These findings demonstrate coordinated temporal patterns between algal growth dynamics and phycosphere microbial succession in pilot-scale wastewater raceways, providing operational insight into microbial community structure under phosphorus-limited phycoremediation.
Abstract
Early detection of lambing is essential for improving animal welfare and farm management, as it enables timely intervention and reduces complications. Wearable inertial sensors have been applied to sheep monitoring, with frequent transitions between standing and lying identified as key behavioral indicators of lambing. However, unlike in larger livestock, no accelerometry-based system currently provides real-time detection for small ruminants, and existing studies remain limited to preliminary approaches. This study monitored 61 ewes using accelerometers sampling at 20 Hz, while lambing was simultaneously recorded on video to establish precise birth times for 113 events. Video analysis also documented litter size and the need for assistance. Data were organized per ewe, supplemented with information such as birth year, previous lambing records, and ultrasound results. A video of one birth was included to illustrate behavior during the process. The dataset provides a valuable foundation for developing algorithms capable of classifying birth-related behaviors, thereby supporting future automated lambing detection systems.
Authors
Xing Wu Tatiana Bellagio Yunru Peng Lucas Czech Meixi Lin Patricia Lang Ruth Epstein Mohamed Abdelaziz Jake Alexander Carlos Alonso-Blanco Heidi Lie Andersen Modesto Berbel Joy Bergelson Oliver Bossdorf Liana Burghardt Mireille Caton-Darby Robert Colautti Carolin Delker Panayiotis G. Dimitrakopoulos Kathleen Donohue Walter Durka Gema Escribano-Avila Steven J. Franks Felix B. Fritschi Alexandros Galanidis Alfredo Garcia-Fernández Ana García-Muñoz Elena Hamann Allison Hutt José M. Iriondo Thomas E. Juenger Stephen R. Keller Karin Koehl Arthur Korte Pamela Korte Alexander Kutschera Carlos Lara-Romero Laura Leventhal Daniel Maag Arnald Marcer Martí March-Salas Juliette de Meaux Belén Méndez-Vigo Javier Morente-López Timothy C. Morton Zuzana Münzbergova Anne Muola Hanna Akiko Nomoto Meelis Pärtel F. Xavier Picó Brandie Quarles-Chidyagwai Marcel Quint Niklas Reichelt Agnieszka Rudak Johanna Schmitt Gregor Schmitz Merav Seifan Basten L. Snoek Remco Stam Marc Stift John R. Stinchcombe Mark A. Taylor Peter Tiffin Irène Till-Bottraud Anna Traveset Jean-Gabriel Valay Martijn Van Zanten Vigdis Vandvik Cyrille Violle Detlef Weigel Maciej Wódkiewicz François Vasseur J. F. Scheepens Moises Exposito-AlonsoAbstract
Climate change forces species to adapt rapidly to avoid extinction. To directly observe rapid adaptation and extinction, we conducted synchronized evolution experiments with Arabidopsis thaliana in 30 locations across Western Europe, the Mediterranean, the Levant, and North America. Whole-genome pooled sequencing of ~70,000 surviving plants revealed repeatable allele frequency shifts in similar climates but divergent shifts across contrasting ones, indicating evolutionary adaptation. We identified genetic variants linked to climate adaptation, including genes involved in processes ranging from thermal-stress sensing to spring-flowering timing. Evolutionary trends were often predictable, but variable, across environments. In warmer climates, evolutionary predictability correlated with population survival over 5 years, whereas erratic changes preceded extinction. These results show that rapid climate adaptation is possible, but understanding its limits will be crucial for biodiversity forecasting.
Abstract
No abstract has been registered
Authors
Lumbani Benedicto Banda Frank Thomas Ndjomatchoua Ritter Atoundem Guimapi Komi Mensah Agboka Abdelmutalab G.A. Azrag Wezi G. Mhango Trust Kasambala Donga Chikondi Makwiza Karl Thunes Elfatih M. Abdel-RahmanAbstract
The cassava whitefly (Bemisia tabaci) greatly constrains cassava production across Africa due to its role as a vector of viral diseases that cause substantial yield losses. Effective management of this insect pest requires detailed knowledge of its spatio-temporal distribution, however long-term datasets are scarce. Mechanistic models circumvent these long-term data needs by modelling temperature-dependent processes that govern population dynamics. Nevertheless, their application to B. tabaci remains poorly explored. Here, we developed a mechanistic model to derive a risk index (RI) for B. tabaci across Africa, focusing on Malawi. The model integrates the effects of temperature on the life stages of B. tabaci to predict temporal risk dynamics and assess climate change impacts. Validation against historical data demonstrated strong agreement, with high cosine similarity values (0.95 in 1988 and 0.96 in 1990) and high correlation coefficients (0.73 and 0.78 in 1988 and 1990, respectively), supporting its suitability as a proxy for whitefly population dynamics. Areas with temperatures between 20.2 °C and 32.5 °C are conducive to B. tabaci population increase, with suitability peaking near 27.5 °C. Cassava-growing regions in central and western Africa experience year-round higher RI values, whereas southeastern Africa experiences peak RI values from October to March. In Malawi, the lakeshore and southern regions were most vulnerable, with RI peaking in these areas during the rainy season. At continental and national scales, climate change is projected to increase RI values. These findings underscore the importance of timing pest control interventions to align with peak risk periods and highlight the utility of mechanistic models for informing region-specific whitefly management strategies.
Authors
Getachew Birhanu AberaAbstract
Anaerobic digestion (AD) is a biological process where microorganisms degrade organic waste under anaerobic condition and produce biogas consisting of 50–75% methane (CH4), 25–50% carbon dioxide (CO2), and other trace gases. However, the presence of non-methane gases reduces the heating value of biogas and impurities, such as H2S, reduces its desirability. To improve the quality, biomethanation could upgrade biogas via converting CO2 using green hydrogen (H2) into additional CH4 by the action of methanogenic archaea. Despite this potential, the presence of process inhibitors like H2S and NH3-N can impact the efficiency of this environmentally friendly method. To address this challenge, the application of biofilm has emerged as a promising approach to improve system performance and stability under varying operational parameters and inhibitory conditions. For instance, a case study from a Norwegian full-scale biofilm plug flow reactor (BPFR) included in this study demonstrated the potential of biofilm-based AD in maintaining stable CH4 yield, even under a higher FOS/TAC ratio of greater than 0.4 and NH3-N concentration of 5500 ppm. Based on this foundation, this PhD study investigated the impact of H2S and NH3-N on biomethanation and the role of biofilm-based biomethanation in mitigating these effects.
Abstract
Excessive phosphorus and nitrogen losses from agricultural areas cause eutrophication, one of the most prevalent global water quality challenges. This study's main goal was to identify and evaluate the occurrence of diffuse pollution attributed to extreme hydrologic events from agriculturally dominated areas. We analyzed the Norwegian Agricultural Environmental Monitoring Program long-term data from four catchments representing different agricultural practices and climate regimes. Extreme flows were set at ≥10% exceedance (Q10) flows and the corresponding nutrient and sediment losses were evaluated. The extreme flow occurrences between the catchments were significantly different, especially in spring and autumn months. In southeastern Norway (cereal crops; distinct winter freeze and thaw), at least 70% of the total suspended sediments (TSS) and total phosphorus (Ptot) losses were attributed to extreme flows. Forty percent of the TSS and nutrient losses were attributed to extreme flows in the grassland-dominated in western Norway (coastal climate, high annual total precipitation). In southern Norway (vegetables, coastal climate), TSS and Ptot losses were largely attributed to extreme events but only 30% of the dissolved phosphorus and 40% of the total nitrogen losses. The occurrence of diffuse pollution due to extreme events varied significantly between agricultural practices and climate regimes. We recommend that the effectiveness and efficiency of conservation measures against extreme hydrologic events in relation to agricultural practices and prevailing climate conditions should be accounted for in planning and implementation.
Authors
Rupesh Kumar Singh Henrique Trindade João Ricardo Sousa Arne Sæbø Vishnu D. Rajput Tao ZhangAbstract
No abstract has been registered
Authors
Mandeep PoudelAbstract
No abstract has been registered
Abstract
Pedotransfer functions (PTFs) are widely used as an efficient alternative for estimating soil hydraulic properties. However, insufficient understanding of how specific input data characteristics drive PTF prediction performance, coupled with challenges in assessing PTF transferability beyond their development datasets, limit their robustness and broad application. Here, we employed the hierarchical Rosetta3 as the development dataset and evaluated its PTF performance using two independent application datasets (National Cooperative Soil Survey (NCSS) and HYBRAS-V2), comprising over 51,900 samples. To further investigate the effect of the input similarity on PTF performance, the Chamfer Distance (CD) was used to quantify the similarity between the development and application datasets. The extensive NCSS database allowed us to stratify the application dataset by soil temperature regimes, texture classes, and depths for a detailed performance evaluation. Results showed that incorporating additional inputs (e.g., bulk density, field capacity, and wilting point) moderately reduces the correlations between these newly added inputs and the estimation residuals, and that higher residual-input correlations are associated with inferior PTF performance. Furthermore, a lower CD (better resemblance of development and application dataset) leads to better PTF performance. However, increasing input complexity using the hierarchical Rosetta3 models mitigates this effect of resemblance, enhancing robustness across diverse soil and environmental conditions. These findings highlight the importance of analyzing residual-input correlations and suggest that quantifying input-data similarity between PTF development and application datasets can serve as a practical approach to assess the transferability of PTFs.