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
Sammendrag
Collection, processing and provision of comprehensive geometric information of forest roads is decisive for its technical classification to facilitate sustainable timber supply chains. An automized classification system based on the mobile proximal sensor platform RoadSens was developed, applied and validated through a case study approach in Eastern Norway. Six sample roads of various vegetation stages were surveyed through RoadSens and complemented through sampled total station measurements for validation purposes. The determined geometric parameters road slope, curvature and width were used for technical classification following the national forest road standard. Road width was identified as the main constraint in meeting the standard, resulting in a general downgrading of the sampled roads according to its technical class. The results showed a root mean square error (RMSE) ranging from ±0.53 to 1.50 m (12–33%) depending on the road and vegetation stage compared to the validation data. Despite these accuracy constraints, the application case study already indicates a general need for improvement of road data acquisition and updating of associated databases. The study underscores that, despite the challenges and limitations, there is a clear need for an automated sensing and classification system, which offers a cost-effective alternative to manual surveying and requires less specialized expertise.
Sammendrag
Aim Four different grassland types of varying land-use intensity and history were investigated for changes in plant species composition and richness over a 7- to 10-year period. Shifts in species occurrence frequencies and species-specific indicator values for nectar production were analyzed to assess how vegetation changes may influence the availability of floral rewards. Location Norwegian mainland. Methods We utilized survey (2004–2008) and resurvey (2011–2018) data from the Norwegian Monitoring Program for Agricultural Landscapes, examining vegetation in managed and unmanaged grasslands from 538 permanent vegetation plots within 97 monitoring squares across Norway. Using species-specific indicator values for nectar production, we tested how compositional changes in vascular plant communities are reflected in the occurrence frequency and cover of pollen- and nectar-providing plants. Results Grassland species composition has slightly shifted toward communities more dominated by later successional species, particularly those associated with shadier and wetter conditions. Major changes in the occurrence frequencies of individual species suggest a decline in pollen and nectar production. Specifically, 29 of 40 species (72.5%) that showed significant decreases in frequency were flowering plants important for pollinators. Across all grassland types, the average cover of pollen- and nectar-producing plants has declined over time, indicating a reduction in floral resources available to pollinating insects. Conclusions Our findings indicate a gradual transition in grassland habitats toward conditions that may be less favorable to pollinators, as reflected by changes in species occurrence frequencies and plant cover. Additionally, plant species associated with moist environments are likely to increase in abundance under continued climate change. This study highlights the value of systematic grassland monitoring within agricultural landscapes as an effective tool for detecting vegetation changes, even over short time spans. Such monitoring supports timely decision-making and the implementation of targeted management strategies to preserve ecologically and economically important habitat types.
Forfattere
Belachew Gizachew ZelekeSammendrag
Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership.
Forfattere
Ulrika Jansson Asplund Damian Petkovic Karlsen Anne Krag Brysting Rune Halvorsen Håvard Kauserud O. Janne Kjønaas Johan AsplundSammendrag
After five centuries of selective cutting in the boreal Fennoscandian forest there was a shift to stand replacing harvest (clear-cutting) in the 1940s. This shift altered light conditions experienced by the forest understory profoundly, from semi-open conditions to light regimes altering between very open in the recently clear-cut forest to very dense some decades later. In this study, we investigated the long-term effects of clear-cutting on vascular plants and bryophytes. Our study system consists of twelve pairs of mesic spruce forests in Southeastern Norway, with a previously clear-cut, but now mature stand and a near-natural forest within each pair. Vascular plant cover was almost twice as high in the near-natural than in the mature, previously clear-cut forest sites, despite similar standing volume and light availability. Overall, previous clear-cutting did not have long-term effects on species richness, but vascular plant species richness was more responsive to soil Ca, a key driver of plant community composition, in the near natural forests. Likewise, the community composition showed a stronger association with soil chemistry in near-natural forests, suggesting that management alters natural drivers of understory communities. The long-lasting effects of clear-cutting was distinct for understory cover and mainly driven by common species such as the keystone species Vaccinium myrtillus, which was substantially less abundant in previously clear-cut stands.
Forfattere
L. Duncanson P. M. Montesano A. Neuenschwander A. Zarringhalam N. Thomas D. M. Minor M. A. Wulder J. C. White E. Guenther T. Feng V. Leitold S. Hancock J. Armston Stefano Puliti A. I. Mandel S. Shah C. Silva M. Purslow J. Bruening Johannes Breidenbach Erik Næsset Svetlana Saarela N. Hunka J. R. Kellner S. P. Healey D. Schepaschenko J. Wallerman C. S. R. Neigh N. Carvalhais R. DubayahSammendrag
Forest aboveground biomass Density (AGBD) maps provide important constraints on climate and carbon cycle models and enable the long-term monitoring of carbon stocks. NASA's latest spaceborne lidar instruments provide unprecedented observations of forest structure that are used to compile spatially continuous, locally trained maps of circa 2020 AGB density and stock. To address a geographical limitation of the International Space Station deployed GEDI instrument, herein we map high northern latitude forests with a fusion of NASA's ICESat-2 mission, Harmonized Landsat Sentinel-2 (HLS), and Copernicus GLO-30 topographic data. We report a domain-wide estimate for boreal forests of 72.96 +/− 0.61 Pg AGB. Combining these maps with those of tropical and temperate forests from GEDI products provides a global estimate of 2020 biomass stocks of 593.49 +/− 11.47 Pg AGB. We analyze biomass means and totals across the boreal domain in different land cover types, slope classes, and ecoregions. We compare these products to national estimates of AGB stocks for high latitude countries, finding good general agreement between national reports and EO-based estimates, particularly for countries with robust National Forest Inventories (NFIs) and boreal forests, while underestimation of high AGBD in tall, dense forests remains a challenge for AGBD mapping. Satellite products can be used to support wide area estimation of aboveground biomass, and the maps described here provide insights into carbon stocks, patterns, and dynamics at scales relevant to forest management. These open access data products serve as a baseline to assess future changes associated with drought, fire, insects, deforestation, and degradation.
Forfattere
Rajiv Chaudhary Maximiliano Estravis Barcala Irena Fundova Tomáš Funda Zhi-qiang Chen Harry X. WuSammendrag
Background Drought intensity and frequency are increasing under global warming in the boreal forests, and breeding for drought resistance will facilitate adaptation of new planting material to changing climate conditions. We used a tree-ring dataset of 559 individuals to study Scots pine genetic variation and the efficiency of genomic selection of drought-response traits (drought resistance, recovery and resilience), for the first time. From genotyping-by-sequencing (GBS), 31,101 SNPs were generated and used for the study. Results Significant genetic variation was detected for drought-response and other growth, wood-anatomy and wood density traits. Heritability estimates for wood-anatomical traits were higher than those for drought-response and growth traits. Genetic correlations between drought-response and wood-anatomical traits were generally high but mostly nonsignificant. In contrast, drought resistance and recovery showed positive and significant correlations with basal area increment and height. We found that the predictive ability and accuracy for drought-response traits were lower than those for wood-anatomical traits, and were comparable between GBLUP and ABLUP. Greater genetic gain per year can be achieved through genomic selection relative to pedigree-based selection if the generation interval is reduced. Conclusions The positive genetic correlation between drought-response and growth traits will enable simultaneous selection for improved growth and increased drought resistant trees in Scots pine breeding through either pedigreed-based and genomic selection.
Sammendrag
No abstract has been registered
Sammendrag
Norway spruce Picea abies is an economically important tree species in Europe, actively managed for forestry. Among the most negative biotic factors for growth and hence forest production is damage caused by wildlife, such as damage through bark stripping by red deer Cervus elaphus. We quantified bark stripping damage on Norway spruce trees in across 450 stands (aged 20–72 years) spanning a 400 km latitudinal range along Norway's west coast and analysed the underlying mechanisms driving increased probability of bark stripping by red deer. A total of 74% of tree stands had bark stripping damage. The mean percentage of damaged trees was 16.0%, but 50 stands (11.1% of the stands) had more than 50.0% damaged trees. The most important factor determining probability for bark stripping was broad-scale red deer density, where the probability increased markedly when density reached approximately two harvested red deer per km2. In addition, proximity to agricultural farmland, distance from roads, site productivity, distance between twig whorls and terrain ruggedness index increased the probability of bark stripping. Our study on bark stripping on Norway spruce highlights the importance of red deer population control, but also the importance of evaluating environmental factors as well as site factors and tree characteristics in forestry planning to mitigate damage from red deer.
Forfattere
Kai Yue Pieter Vangansbeke Isla H. Myers-Smith Donald M. Waller Kris Verheyen Markus Bernhardt-Römermann Lander Baeten Ingmar R. Staude Anne D. Bjorkman Radim Hédl Christopher Andrews Elena Barni Thomas Becker Antoine Becker-Scarpitta José Luis Benito-Alonso Jonathan Bennie Imre Berki Volker Blüml Jörg Brunet James M. Bullock Hans Van Calster Michele Carbognani Markéta Chudomelová Déborah Closset-Kopp Pavel Dan Turtureanu Gergana N. Daskalova Guillaume Decocq Jan Dick Martin Diekmann Thomas Dirnböck Tomasz Durak Ove Eriksson Brigitta Erschbamer Bente Jessen Graae Thilo Heinken Martin Hermy Peter Horchler Ute Jandt Bogdan Jaroszewicz Róbert Kanka Jozef Kollár Martin Kopecký Thomas Kudernatsch Andrea Lamprecht Jonathan Lenoir Martin Macek Marek Malicki František Máliš Ottar Michelsen Fraser Mitchell Tobias Naaf Thomas A. Nagel Miles Newman Adrian C. Newton Lena Nicklas Ludovica Oddi Anna Orczewska Simone Orsenigo Adrienne Ortmann-Ajkai Jan den Ouden Harald Pauli George Peterken Petr Petřík Remigiusz Pielech Mihai Puşcaş Christophe Randin Kamila Reczyńska Christian Rixen Fride Høistad Schei Wolfgang Schmidt Jan Šebesta Alina Stachurska-Swakon Tibor Standovár Krzysztof Świerkosz Balázs Teleki Jean-Paul Theurillat Tudor-Mihai Ursu Thomas Vanneste Mark Vellend Philippine Vergeer Ondřej Vild Luis Villar Pascal Vittoz Manuela Winkler Sonja Wipf Fuzhong Wu Shengmin Zhang Pieter De FrenneSammendrag
Climate warming is shifting biological communities, with warmth-demanding species being favoured at the expense of cold-adapted species in a process referred to as thermophilization1,2,3,4. Because biodiversity responses often lag behind climate warming, climatic debts are accumulating in many ecosystems across the world5,6,7. Although we might expect that thermophilization and climatic debts will vary among habitats, standardized quantification across ecosystems is lacking. Here we analysed multidecadal data from 6,067 resurveyed vegetation plots over 12–78 years in forests, grasslands and on alpine summits across Europe. We demonstrate that forest understory and grassland plant communities experienced positive thermophilization, although not significantly different from zero. By contrast, alpine summit vegetation showed much stronger (up to five times) and significant thermophilization. Thermophilization was driven largely by increases in warmth-demanding species in grasslands, by declines in cold-adapted species on alpine summits and by both processes in forests. Significant climatic debts have accumulated in forests and alpine summits, but less so in grasslands, with debts positively correlated with macroclimate temperature changes. Our findings uncover divergent thermophilization trajectories and increasing climatic debts across ecosystems. Moreover, we highlight the mechanisms that enable some communities to track climate change more closely than others and provide a basis for projecting future shifts in plant communities under accelerating climate warming.
Forfattere
Björn H. Franke Aafke M. Schipper Tal Avgar Luca Börger Nilanjan Chatterjee Thomas Müller Brian J. Smith Briana Abrahms Abdullahi H. Ali Nina Attias Hattie L. A. Bartlam‐Brooks Floris M. van Beest Jerrold L. Belant Dean E. Beyer Niels Blaum Michael B. Brown Bayarbaatar Buuveibaatar Francesca Cagnacci Simon Chamaillé‐Jammes Nandintsetseg Dejid Jasja Dekker Arnaud L. J. Desbiez Julian Fennessy Christina Fischer Ilya Fischhoff Adam T. Ford Benedikt Gehr Jacob R. Goheen Ronaldo Gonçalves Morato Mark Hebblewhite Robert Hering Marco Heurich A. J. Mark Hewison Lynne A. Isbell Matthew Kauffman Andrew Jakes René Janssen Paul F. Jones Bob Jonge Poerink Clayton Lamb John Durrus Linnell A. Catherine Markham Courtney J. Marneweck Jenny Mattisson John McEvoy Erling Meisingset Evelyn Merrill Guilherme de Miranda Mourão Bram Van Moorter Nicolas Morellet Atle Mysterud John Odden Kirk A. Olson Agustín Paviolo Tyler Petroelje Kelly M. Proffitt Kasim Rafiq Nathan Ranc Christer Moe Rolandsen Daniel I. Rubenstein Sonia Saïd Hall Sawyer Niels Martin Schmidt Nuria Selva Agnieszka Sergiel Erling Johan Solberg Melissa Songer Jonas Stiegler Olav Strand Siva Sundaresan Jeffrey J. Thompson Wiebke Ullmann Dorj Usukhjargal Ulrich Voigt Filip Zięba Tomasz Zwijacz‐Kozica Mark A. J. Huijbregts Marlee A. TuckerSammendrag
Aim Animal movements are a fundamental process affecting communities and ecosystems. Quantifying habitat selection across species and habitats is key for understanding how animals respond to environmental change. Currently, we lack comparative studies that examine how habitat selection varies across species traits and landscapes. We aim to quantify global patterns of habitat selection to help understand the fundamental drivers of movement behaviour. Location Global. Time Period Contemporary. Major Taxa Studied Terrestrial mammals. Methods We estimated selection coefficients for terrain ruggedness, vegetation productivity, human population density and distance to roads of individual terrestrial mammals through step‐selection analysis of 1344 GPS tracks across 48 species. We quantified intra‐ and interspecific variation and tested whether selection coefficients were associated with species traits and habitat availability. Results We observe an overall avoidance of roads and areas of high human population density as well as rugged terrain, with a large proportion of individuals selecting for areas of intermediate NDVI. However, we also found large intraspecific variation in habitat selection and show that this variation is predicted by local and landscape‐level environmental conditions rather than species traits. Individuals in more remote areas exhibited weaker functional responses to human presence than those in more disturbed areas. Avoidance of rugged terrain is also context‐dependent, with stronger avoidance when local ruggedness is high. The only exception to the observed intraspecific variability is consistent species‐level responses to road proximity. Main Conclusions Our findings contribute to the understanding of habitat selection by terrestrial mammals, showing that selection is largely shaped by environmental conditions and that animals exhibit high plasticity in their responses. Our results also provide further evidence for the significant impact of roads on animal movement. These insights can help us to understand the potential effects of environmental change on the behaviour of mammal species around the world.