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
Abstract
Existing methods for resource nexus analysis do not cover all aspects of complex resource management problems. Key methodological challenges include setting the scale, scope, and resolution of a nexus analysis, as well as adequately representing the quantity and quality of resource interactions. Additionally, determining the degree of collaborative governance for resource management, accounting for the role of existing policies, and developing robust scenarios for future predictions are also crucial constraints. To address these limitations, we developed a conceptual model of the resources nexus for Otta valley in Norway, an area characterized by resource use trade-offs across interconnected systems. We introduced the concept of ‘‘resource scapes’’ which is the physical availability, key interactions, management networks, and policies governing a resource at a specific time and place. We defined resource scapes for water, energy, and biomass resources in the studied area. Employing stock and flow loops, social network analysis, material flow accounting, and policy reviews, we developed the model in a layered topology using the coupled component modeling approach. In addition, we developed future resource scenarios nested within national pathways– the Norwegian nexus pathways (NNPs)– aligned with the five globally adopted shared-socioeconomic pathways (SSPs), using a narrative downscaling approach. Our results show that annual variations in resource balances are connected to changing externalities. A low Network External-Internal (EI) index (0.392) indicates weak overall collaborative governance of nexus resources. Our modeling framework (1) addresses limitations in current nexus methods, (2) facilitates testing of alternative policy interventions under future scenarios, and (3) provides a framework for development of integrated assessment models. This approach merges the concept of nexus governance with integrated assessment modeling, thereby enhancing the application of nexus approach for efficient resource management which will be crucial in future as climate and socioeconomic conditions evolve.
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.
Abstract
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.
Authors
Stefano Puliti Emily R. Lines Jana Müllerová Julian Frey Zoe Schindler Adrian Straker Matthew J. Allen Lukas Winiwarter Nataliia Rehush Hristina Hristova Brent Murray Kim Calders Nicholas Coops Bernhard Höfle Liam Irwin Samuli Junttila Martin Krůček Grzegorz Krok Kamil Král Shaun R. Levick Linda Luck Azim Missarov Martin Mokroš Harry J. F. Owen Krzysztof Stereńczak Timo P. Pitkänen Nicola Puletti Ninni Saarinen Chris Hopkinson Louise Terryn Chiara Torresan Enrico Tomelleri Hannah Weiser Rasmus AstrupAbstract
1. Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. 2. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5). 3. 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. 4. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods.
Authors
Shuang Yin Xinli Chen Gabin Piton César Terrer Zhenghu Zhou Gerlinde B. De Deyn Isabelle Bertrand Daniel Rasse Ji Chen Jose Antonio Navarro-Cano Diego AbalosAbstract
Increasing species diversity in agroecosystems appears as a promising venue to restore or increase soil organic carbon (SOC). It has been hypothesized that this effect is largely driven by the greater variation of root systems in plant mixtures, which may promote complementarity. However, the magnitude of this synergistic effect and the root traits driving it are uncertain. The objective of this study is to determine which root trait composition optimizes plant mixture effects on SOC. To do so, we combined a global meta-analysis of 407 paired SOC content observations under mixed species vs. monocultures across grasslands and croplands, and root traits extracted from the GRooT database. The results show that high root mycorrhizal colonization and root tissue density for the species in the mixture have higher positive effects on SOC content. Our analysis also indicates that combining species with high similarity for these traits represents a preferable trait combination to increase SOC with plant mixtures, challenging the current paradigm around plant trait complementarity effects. We observed that the positive response of SOC content to species mixtures was tightly associated with increased root biomass and soil microbial biomass carbon, indicating an important contribution of belowground and microbial residuals to SOC. Additionally, SOC enhancements by plant species mixtures were more likely to be realized in regions with high precipitation, clay-rich soils, and when legumes are present. Our meta-analysis lays out a root-trait framework to enhance SOC with plant mixtures, which can serve as a guide for species and variety selection for field experiments and on-farm applications.
Abstract
Potato Fusarium dry rot and wilt are the most important soil- and seed-borne diseases in potatoes. They cause high economic losses during potato growth and storage across the world. Previous observations have shown that dryocrassin ABBA can induce resistance in potatoes. However, little is known about whether dryocrassin ABBA can suppress Fusarium oxysporum. In this research, we determined that exogenous dryocrassin ABBA significantly inhibited the mycelial growth, changed the cell ultrastructure, increased the MDA content, and decreased the antioxidant enzyme activity of F. oxysporum. The transcriptome analysis of F. oxysporum with or without dryocrassin ABBA indicated that 1244 differentially expressed genes (DEGs) were identified, of which 594 were upregulated and 650 were downregulated. GO term analysis showed that the DEGs were mostly related to biological processes. The KEGG pathway was mainly related to carbohydrate, amino acid, and lipid metabolism. Moreover, most of the expressions of PCWDEs, HSPs, and MFS were downregulated, decreasing the stress capacity and weakening the pathogenicity of F. oxysporum with dryocrassin ABBA treatment. These findings contribute to a new understanding of the direct functions of dryocrassin ABBA on F. oxysporum and provide a potential ecofriendly biocontrol approach for potato Fusarium dry rot and wilt
Authors
Susanne Suvanto Adriane Esquivel-Muelbert Mart-Jan Schelhaas Julen Astigarraga Rasmus Astrup Emil Cienciala Jonas Fridman Helena M. Henttonen Georges Kunstler Gerald Kändler Louis A. König Paloma Ruiz-Benito Cornelius Senf Golo Stadelmann Ajdin Starcevic Andrzej Talarczyk Miguel A. Zavala Thomas A. M. PughAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
• In this book we summarize peer-reviewed scientific articles and research reports from Finland, Sweden, and Norway on continuous cover forestry (CCF), i.e. forestry without clearcutting • This book originates from growing interest in CCF among various stakeholders, and aims to promote discussion, further research, and inform decision-makers • The book targets those interested in boreal forests, forest management, and ecosystem services • In this chapter we review the background to the use of CCF and the reasons that led to its prohibition and subsequent resurgence in the Nordic countries
Abstract
This open access book compiles the latest research on continuous cover forestry in boreal forests, highlighting both the need for additional information and the exciting possibilities that this method presents. Experts in the field explore topics such as forest regeneration, genetic effects, wood production and yield, wood harvesting, forest damage agents, biodiversity, water effects, carbon cycles of forests, economics, forest planning methods, multiple uses of forests, and forest owners' attitudes. As the world faces increasing pressure to balance the multiple goals of forest management, including raw material production, carbon sequestration, biodiversity, and climate change adaptation, it is becoming clear that different forest management methods are required. Even-aged forest management is well-researched, but continuous forest management is a newer and rapidly evolving approach that is gaining popularity in boreal forests. While an overall synthesis of the subject is not yet possible, this book provides an essential foundation for understanding the current state of continuous cover forestry in boreal forests. With the new research data being accumulated all the time, this book is an invaluable resource for researchers, policymakers, and forest managers who want to stay up-to-date on this important topic.