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
Forfattere
Alexey Mikaberidze Christian Cruz Ayalsew Zerihun Abel Barreto Pieter S. Beck Rocío Calderón Carlos Camino Rebecca Campbell Stephanie Delalieux Frederic Fabre Elin Falla Stuart Fraser Kaitlin Gold Carlos Gongora-Canul Frédéric Hamelin Dalphy Ondine Camira Harteveld Cheng-Fang Hong Melen Leclerc Da-Young Lee Murillo Lobo Jr Anne-Katrin Mahlein Emily McLay Paul Melloy Stephen Parnell Uwe Rascher Jack Rich Irene Sarlotti Samuel Soubeyrand Susie Sprague Antony Surano Sandhya Takooree Thomas Taylor Suzanne Touzeau Pablo Zarco-Tejada Nik CunniffeSammendrag
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.
Sammendrag
No abstract has been registered
Forfattere
H. Heinemann F. Durand-Maniclas F. Seidel F. Ciulla Teresa Gómez de la Bárcena M. Camenzind S. Corrado Z. Csűrös Zs. Czakó D. Eylenbosch Andrea Ficke C. Flamm J.M. Herrera V. Horáková A. Hund F. Lüddeke F. Platz B. Poós Daniel Rasse Silva-Lopes da Silva-Lopes M. Toleikiene A. Veršulienė M. Visse-Mansiaux K. Yu J. Hirte A. DonSammendrag
No abstract has been registered
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No abstract has been registered
Sammendrag
Green roofs provide vital functions within the urban ecosystem, from supporting biodiversity, to sustainable climate-positive ESS provisioning. However, how plant communities should best be designed to reach these objectives, and how specific green roof systems vary in their capacity to support these functions is not well understood. Here we compiled data on plant traits and plant–insect interaction networks of a regional calcareous grassland species pool to explore how designed plant communities could be optimised to contribute to ecological functionality for predefined green roof solutions. Five distinct systems with practical functionality and physical constraints were designed, plant communities modelled using object-based optimization algorithms and evaluated using five ecological functionality metrics (incl. phylogenetic and structural diversity). Our system plant communities supported a range of plant–insect interactions on green roofs, but not all species were equally beneficial, resulting in wide-ranging essentiality and redundancy in ecological processes. Floral traits were not predictive of pollinator preferences, but phylogeny was observed to govern the preferences. Large differences in ecological functionality can be expected between green roofs depending on system design and the extent of the plant community composition. Multifunctionality covariance diverged between systems, suggesting that ecological functionality is not inherently universal but dependent on structural limitations and species pool interactions. We conclude that informed system design has a potential to simultaneously support ecosystem services and urban biodiversity conservation by optimising green roof plant communities to provide landscape resources for pollinating insects and herbivores.
Forfattere
Klaus Steenberg Larsen Carl-Fredrik Johannesson Jenni Nordén Holger Lange Hanna Marika SilvennoinenSammendrag
Non-steady-state chambers are widely used for measuring the exchange of greenhouse gases (GHGs) between soils or ecosystems and the atmosphere. It is known that non-steady-state chambers induce a non-linear concentration development inside the chamber after closure, even across short chamber closure periods, and that both linear and non-linear flux estimates are impacted by the chamber closure period itself. However, despite the existence of recommendations on how long to keep the chamber closed, it has been little explored to what extent the length of the chamber closure period affects the estimated flux rates, and which closure periods may provide the most accurate linear and non-linear flux estimates. In the current study, we analyzed how linear regression and Hutchinson and Mosier (1981) modeled flux estimates were affected by the length of the chamber closure period by increasing it in increments of 30 s, with a minimum and maximum chamber closure period of 60 and 300 s, respectively. Across 3,159 individual soil CO2 and CH4 flux measurements, the effect of chamber closure period length varied between 1.4–8.0% for linear regression estimates and between 0.4–17.8% for Hutchinson–Mosier estimates and the largest effect sizes were observed when the measured fluxes were high. Both linear regression and Hutchinson–Mosier based flux estimates decreased as the chamber closure period increased. This effect has been observed previously when using linear regression models, but the observed effect on Hutchinson-Mosier modeled estimates is a novel finding. We observed a clear convergence between the short-period linear regression estimates and the long-period Hutchinson–Mosier estimates, showing that closure periods as short as possible should be used for linear regression flux estimation, while ensuring long-enough closure periods to observe a stabilization of flux estimates over time when using the Hutchinson-Mosier model. Our analysis was based on soil flux measurements, but because the perturbation of the concentration gradient is related to the non-steady-state chamber technique rather than the measured ecosystem component, our results have implications for all flux measurements conducted with non-steady-state chambers. However, optimal chamber closure times may depend on individual chamber designs and analyzer setups, which suggests testing individual chamber/system designs for optimal measurement periods prior to field application
Forfattere
Heidi Kreibich Murugesu Sivapalan Amir AghaKouchak Nans Addor Hafzullah Aksoy Berit Arheimer Karsten Arnbjerg-Nielsen Cynthia Vail-Castro Christophe Cudennec Mariana Madruga de Brito Giuliano Di Baldassarre David C. Finger Keirnan Fowler Wouter Knoben Tobias Krueger Junguo Liu Elena Macdonald Hilary McMillan E. Mario Mendiondo Alberto Montanari Marc F. Muller Saket Pande Fuqiang Tian Alberto Viglione Yongping Wei Attilio Castellarin Daniel Peter Loucks Taikan Oki María J. Polo Huub Savenije Anne F. Van Loon Ankit Agarwal Camila Alvarez-Garreton Ana Andreu Marlies H. Barendrecht Manuela Brunner Louise Cavalcante Yonca Cavus Serena Ceola Pedro Chaffe Xi Chen Gemma Coxon Zhao Dandan Kamran Davary Moctar Dembélé Benjamin Dewals Tatiana Frolova Animesh K. Gain Alexander Gelfan Mohammad Ghoreishi Thomas Grabs Xiaoxiang Guan David M. Hannah Joerg Helmschrot Britta Höllermann Jean Hounkpè Elizabeth Koebele Megan Konar Frederik Kratzert Sara Lindersson Maria Carmen Llasat Alessia Matanó Maurizio Mazzoleni Alfonso Mejia Pablo Mendoza Bruno Merz Jenia Mukherjee Farzin Nasiri Saleh Bertil Nlend Rodric Merime Nonki Christina Orieschnig Katerina Papagiannaki Gopal Penny Olga Petrucci Rafael Pimentel Sandra Pool Elena Ridolfi Maria Rusca Nivedita Sairam Adarsh Sankaran Namboothiri Ana Carolina Sarmento Buarque Elisa Savelli Lukas Schoppa Kai Schröter Anna Scolobig Mojtaba Shafiei Anna E. Sikorska-Senoner Magdalena Smigaj Claudia Teutschbein Thomas Thaler Andrijana Todorovic Faranak Tootoonchi Roshanak Tootoonchi Elena Toth Ronald van Nooijen Franciele Maria Vanelli Nicolás Vásquez David W. Walker Marthe Wens David J. Yu Heidar Zarei Changrang Zhou Günter BlöschlSammendrag
No abstract has been registered
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No abstract has been registered
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No abstract has been registered
Sammendrag
Algal-based wastewater remediation systems (phycoremediation) include phycosphere bacterial communities that influence algal growth, pollutant remediation, and downstream applications of biomass as fertilizers or bio-stimulants. This study investigated the bacterial community dynamics in a novel phycoremediation system using a co-culture of the green algae Stigeoclonium sp. and Oedogonium vaucheri. Bacterial abundance was estimated using flow cytometry (FCM), while community composition was assessed through 16S rRNA gene metabarcoding. Additionally, 28 bacterial strains were isolated from the bioremediation experiment, cultured, genetically characterized for identification and screened for production of the auxin phytohormone indole-3-acetic acid (IAA). Metabarcoding showed that the free-living bacterial community consisted of bacteria from both the wastewater effluent and the algal inocula, while the attached phycosphere community was dominated by bacteria from the algal inocula, indicating the stability of the algae-associated phycosphere. Taxa known to include plant growth-promoting bacteria (PGPB) were abundant, and several strains produced IAA. The bacterial community composition, combined with the potential production of phytohormone by isolated bacteria indicates symbiotic or commensal algae-microbe interactions within the phycosphere bacterial communities. Sterile filtration of wastewater effluent, including only the algal inoculum bacterial communities, reduced algal biomass production and increased bacterial abundance. This study highlights the critical role of microbial interactions in engineered ecosystems and provides insights for optimizing algal-based wastewater treatment technologies.