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.
2024
Abstract
No abstract has been registered
Authors
Wim De Schuyter Emiel De Lombaerde Leen Depauw Pallieter De Smedt Alina Stachurska-Swakoń Anna Orczewska Balázs Teleki Bogdan Jaroszewicz Déborah Closset František Máliš Fraser Mitchell Fride Høistad Schei George Peterken Guillaume Decocq Hans Van Calster Jan Šebesta Jonathan Lenoir Jörg Brunet Kamila Reczyńska Krzysztof Świerkosz Martin Diekmann Martin Kopecký Markéta Chudomelová Martin Hermy Martin Macek Miles Newman Monika Wulf Ondřej Vild Ove Eriksson Peter Horchler Petr Petrik Remigiusz Pielech Thilo Heinken Thomas Dirnböck Thomas A. Nagel Tomasz Durak Tibor Standovár Tobias Naaf Wolfgang Schmidt Lander Baeten Pieter De Frenne Markus Bernhardt-Römermann Radim Hédl Don Waller Kris VerheyenAbstract
1. Wild pollinators are crucial for ecosystem functioning and human food production and often rely on floral resources provided by different (semi-) natural ecosystems for survival. Yet, the role of European forests, and especially the European forest herb layer, as a potential provider of floral resources for pollinators has scarcely been quantified. 2. In this study, we measured the potential nectar production (PNP) of the forest herb layer using resurvey data across 3326 plots in temperate forests in Europe, with an average time interval of 41 years between both surveys in order to assess (i) the importance of the forest herb layer in providing nectar for wild pollinators, (ii) the intra-annual variation of PNP, (iii) the overall change in PNP between survey periods and (iv) the change in intra-annual variation of PNP between survey periods. The PNP estimates nectar availability based on the relative cover of different plant species in the forest herb layer. Although PNP overestimates actual nectar production, relative differences amongst plots provide a valid and informative way to analyse differences across time and space. 3. Our results show that the forest herb layer has a large potential for providing nectar for wild pollinator communities, which is greatest in spring, with an average PNP of almost 16 g sugar/m2/year. However, this potential has drastically declined (mean plot-level decline >24%). 4. Change in light availability, associated with shifts in canopy structure and canopy composition, is the key driver of temporal PNP changes. 5. Synthesis. Our study shows that if management activities are carefully planned to sustain nectar-producing plant species for wild pollinators, European forest herb layers and European forests as a whole can play key roles in sustaining wild pollinator populations.
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Authors
Sylwia Wierzcholska Patryk Czortek Amy Elizabeth Eycott Fride Høistad Schei John-Arvid Grytnes Bogdan JaroszewiczAbstract
No abstract has been registered
Authors
Pablo Moreno-García Flavia Montaño-Centellas Yu Liu Evelin Y. Reyes-Mendez Rohit Raj Jha Robert P. Guralnick Ryan Folk Donald M. Waller Kris Verheyen Lander Baeten Antoine Becker-Scarpitta Imre Berki Markus Bernhardt-Römermann Jörg Brunet Hans Van Calster Markéta Chudomelová Deborah Closset Pieter De Frenne Guillaume Decocq Frank S. Gilliam John-Arvid Grytnes Radim Hédl Thilo Heinken Bogdan Jaroszewicz Martin Kopecký Jonathan Lenoir Martin Macek František Máliš Tobias Naaf Anna Orczewska Petr Petřík Kamila Reczyńska Fride Høistad Schei Wolfgang Schmidt Alina Stachurska-Swakoń Tibor Standovár Krzysztof Świerkosz Balázs Teleki Ondřej Vild Daijiang LiAbstract
Biological nitrogen fixation is a fundamental part of ecosystem functioning. Anthropogenic nitrogen deposition and climate change may, however, limit the competitive advantage of nitrogen-fixing plants, leading to reduced relative diversity of nitrogen-fixing plants. Yet, assessments of changes of nitrogen-fixing plant long-term community diversity are rare. Here, we examine temporal trends in the diversity of nitrogen-fixing plants and their relationships with anthropogenic nitrogen deposition while accounting for changes in temperature and aridity. We used forest-floor vegetation resurveys of temperate forests in Europe and the United States spanning multiple decades. Nitrogen-fixer richness declined as nitrogen deposition increased over time but did not respond to changes in climate. Phylogenetic diversity also declined, as distinct lineages of N-fixers were lost between surveys, but the “winners” and “losers” among nitrogen-fixing lineages varied among study sites, suggesting that losses are context dependent. Anthropogenic nitrogen deposition reduces nitrogen-fixing plant diversity in ways that may strongly affect natural nitrogen fixation.
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Authors
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.
Abstract
This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis. Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers. Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods. In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.
Abstract
PixSim is a flexible, open-source forest growth simulator designed to operate at the pixel level of high-resolution, wall-to-wall forest resource maps generated through remote sensing approaches. PixSim addresses the need to adapt forest growth simulators to the data produced by modern remote sensing-based forest inventories, rather than relying on stand-level averages from traditional field-based inventories. By operating at the pixel level, PixSim captures intra-stand variability in high-resolution forest resource maps, which is often overlooked by stand-level simulators. This capability aligns with the current focus on precision forestry, aimed at improving management decisions with localized data and small-scale management. Implemented in the R programming language, PixSim features minimal package dependencies, provides flexibility and scalability, and has been optimized for high-resolution, large-scale simulations, ensuring efficient computation. The simulator’s flexibility and open-source nature support the incorporation of management modules and the inclusion of climate change scenarios in simulations.
Authors
Fride Høistad Schei Mie Prik Arnberg John-Arvid Grytnes Maren Stien Johansen Jørund Johansen Anna Birgitte Milford Anders Røynstrand Mari Mette TollefsrudAbstract
Climate change and human activities have accelerated the spread of non-native species, including forest pests and pathogens, significantly contributing to global biodiversity loss. Pathogens pose a significant threat to forest ecosystems due to a lack of coevolution with native hosts, resulting in ineffective defence mechanisms and severe consequences for the affected tree species. Ash dieback, caused by the fungus Hymenoscyphus fraxineus, is a relatively new invasive forest pathogen threatening ash (Fraxinus excelsior) with mortality rates in northern Europe reaching up to 80 %. The loss of ash due to dieback has severe ecological implications, potentially leading to an extinction cascade as ash provides crucial habitats and resources for many organisms. Despite this, the consequences of ash dieback on associated communities are largely unknown. To address this, we analysed changes in species richness, vegetation structure, and composition in 82 permanent vegetation plots across 23 Norwegian woodlands. We compared data collected before and 10–14 years after the emergence of ash dieback. In these woodlands, ash significantly declined in cover, leading to changes in tree species composition and facilitating the establishment of other woody tree species like hazel (Corylus avellana) and the invasive species sycamore (Acer pseudoplatanus). Despite these changes in the tree species composition, no significant alterations were observed in the understory plant community, indicating a degree of ecosystem resilience or a lagging community response. At this point, and with our focus on the vascular plants, we do not find support for cascading effects due to ash dieback. However, our findings demonstrate that one invasive species is facilitating the expansion of another, raising concerns about potential ecological imbalance and cascading effects in the future.