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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2026

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Forest disturbances have increased in many regions, but how they impact habitat suitability for wildlife remains poorly understood. Here, by combining tracking data on 3,069 individuals of four ungulate species (European bison, moose, red deer and roe deer) with satellite-based maps, we perform a continental, multi-decadal assessment of large herbivore responses to forest disturbance. Despite strong intraspecific variation, all species show an increased selection of disturbed areas for ≥35 years after disturbance. Although the patterns closely reflect species-specific foraging strategies, all species selected more strongly for smaller disturbance patches, depending on the availability of alternative foraging habitats (grasslands and croplands). Model projections across the species’ range extents show positive but regionally varying effects of forest disturbances on habitat suitability between 2000 and 2023. Our findings indicate that forest disturbances can attract large herbivores and that the recent increase in forest disturbances improved habitat suitability for our study species across Europe, highlighting the importance of considering long-term disturbance-related dynamics for wildlife and forest management. Given expected future increases in disturbance, resulting habitat improvements could amplify conflicts with forestry, but also contribute to restoring large herbivores and their ecological functions.

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Amid increasing temperatures and extended drought periods, forest managers require comprehensive information regarding the suitability of various tree species under changing climatic conditions. To address this need, we assembled a unique dataset spanning Europe, incorporating multiple data sources such as national forest inventories, forest management plans, and data from ICP Forests. Our database ultimately included over six million individual trees across 860,000 forest plots throughout Europe. Using this extensive dataset, we developed Species Distribution Models (SDM) for 30 and Site Index Models (SIM) for 25 European tree species, the latter limited by data availability. Both model types were used to generate predictions at a spatial resolution of 1 × 1 km for the periods 2011–2040, 2041–2070, and 2071–2100 under climate change scenarios RCP2.6, RCP4.5 and RCP8.5. The model predictions aim to estimate the top height and assess climate suitability across Europe under future climate conditions. One potential application of these models is in a decision support system (DSS) to inform tree species selection and management strategies in the context of climate change. Provided are the models, prediction outputs, and supporting information, as the underlying database is restricted by data use agreements.

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The availability of reliable ground-truth data is one of the main bottlenecks for improving high-resolution forest attribute maps from Earth observation data. This is underpinned by the European Union (EU) Forest Strategy for 2030 that underscores the need for harmonized, cross-border forest resource assessments that integrate both remote sensing and field-based National Forest Inventory (NFI) data. However, confidentiality constraints on NFI plot coordinates present a significant barrier to aligning these datasets, thereby limiting the development of unified forest monitoring systems that can fully leverage the potential of Earth Observation data. To overcome these data-sharing limitations we explored the effectiveness of a privacy-enhancing technique, known as Federated Learning (FL), that is a form of distributed computing aimed at preserving the privacy and confidentiality of data owned by different organizations. This methodology has been tested for the collaborative modelling and mapping of forest timber volume across four European countries: Norway, Sweden, Finland, and Italy. We employed a time-series convolutional neural network (CNN) architecture tailored to integrate 40 years of Landsat or 7 years of Sentinel imagery and terrain variables with harmonized NFI data from more than 85,000 sample plots. This model architecture was used for the FL approach and compared to traditional country-specific and centralized modelling strategies. FL models achieved predictive performances comparable to the traditional models, which proofs the effectiveness of the proposed approach. Centralized or global models showed slightly reduced performance compared to the national models, highlighting the value of fine-tuning with local ground-truth data. By aligning with the EU’s forest monitoring objectives, FL facilitates the generation of harmonized models and maps of forest features, like timber volume and biomass, that are critical to support evidence-based forest policy and management. The findings underscore the potential of FL to transform collaborative environmental monitoring, particularly in domains where data confidentiality and interoperability are critical.

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Individual tree structure plays a key role in forest monitoring, biomass estimation, and ecological assessment. However, ground-based remote sensing methods such as terrestrial and mobile laser scanning frequently produce incomplete point clouds due to occlusion, particularly in the upper canopy. This limits the accuracy of derived structural metrics such as tree height or crown volume. In this study, we present a novel deep learning-based method to reconstruct the outer crown shape of coniferous trees from incomplete point clouds. Instead of completing the full tree structure, we focus on predicting the alpha-shape of the crown, enabling a more efficient and generalizable approach for structural reconstruction. We train a geometry-aware transformer model (AdaPoinTr) on synthetically generated partial tree crowns and evaluate its performance across three independent datasets encompassing different forest types and acquisition conditions. The model consistently improved the similarity metric Chamfer distance (CD) between partial and predicted tree crown shapes and reduced height estimation errors compared to using partial data alone (reduced bias from -11% to -3.5%). Our results demonstrate that this shape-based strategy enables the extraction of key tree-level parameters from incomplete data, offering a practical solution for gaining improved 3D forest structural information from cost-sensitive or logistically constrained forest monitoring acquisitions.

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Are ecological scaling relationships stable through time, or can shifting parameters signal “quiet” community reorganization before diversity loss becomes evident? Using a century-long resurvey of subalpine and alpine vegetation along an elevational gradient in Norway, we tested whether key scaling relationships linking (I) spatial extent to diversity, (II) elevational distance to compositional and functional dissimilarity, and (III) species commonness to occupancy have remained stable through time. In 2008 and 2022, we resurveyed semi-permanent plots in central Norway first sampled in 1923–1933 along 995–1495 m.a.s.l., aggregating plots into 50-m elevational belts. We quantified spatial scaling of diversity with area, community differences across elevation, and relationships linked to species commonness and occupancy. Scaling relationships revealed a reorganization that is not captured by richness alone. Large-scale community structuring across elevation remained remarkably stable over the past century, with largely unchanged species– area relationships, beta and zeta diversity, and abundance–occupancy relationships. Despite this, absolute dissimilarity across elevational belts declined with beta diversity increasingly driven by species turnover while the nestedness component weakened. Communities also became progressively characterised by more widespread, common species and a more even abundance distribution, in addition to a convergence of ecological preferences. While the elevational gradient has not collapsed into a uniform assemblage, key scaling parameters within the gradient have shifted. These changes suggest a gradual compositional and functional reorganization of the vegetation that might for now maintain ecosystem functioning through dominant generalists, while quietly eroding long-term ecosystem resilience through species losses.

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Anthropogenic land conversion is putting increasing pressure on wildlife populations around the world. To mitigate impacts, it is necessary to develop a detailed mechanistic understanding of how animals are affected by different types of human activity. A key challenge is to disentangle the effects of static infrastructure, like roads or buildings, and the presence of humans in the landscape. To address this question, we examined if terrestrial mammals altered their movement behaviour around buildings in response to reduced human mobility during COVID‐19 lockdowns. We compiled GPS tracking data from 35 study sites across five continents, for 10 carnivore species and 13 herbivore species, totalling >1 million location records from 586 individuals. For each study, we used integrated step selection analysis to test the extent to which animals changed their avoidance of buildings as lockdown took effect, leveraging the recently released Microsoft MLBuildings dataset of global building locations. Analysis of population‐level effects revealed that, in areas with high Human Footprint Index (HFI), animals tended to show a significant reduction in their avoidance of buildings during lockdown, but not in low HFI areas. No such trend was detected during equivalent periods in years other than 2020, indicating that behavioural changes were a result of reduced human mobility during lockdowns. Overall, our findings suggest that animals living alongside humans exhibit greater plasticity when people change their behaviour, likely indicating the combined effects of environmental filtering and habituation. More generally, our study provides a critical first step towards developing evidence‐based tools for forecasting how wildlife movement behaviour may change in response to different land‐use strategies, human activities, conservation interventions or environmental perturbations.

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In modern agricultural landscapes, «the green in between» plays a crucial role in supporting the survival of many plant and animal species. These areas include field margins, grassy banks between fields, mid-field islands of uncultivated vegetation, rocky outcrops, fallow land, and other patches of unused ground. We surveyed vascular plant species in these green spaces and compared the results with a similar survey conducted 18 years earlier. Our findings reveal a general decline in species richness, including many plants important for pollinators. Nevertheless, numerous green patches remain species-rich and continue to provide valuable resources for pollinators. Among these, road verges stood out as the most diverse. This suggests that active management of the green in between—such as removing invasive species and implementing regular mowing to maintain flower-rich patches—could significantly enhance its value for wildlife and biodiversity conservation.

Sammendrag

Rapporten sammenfatter resultater fra utredningsprosjektet «Jordbruk og karbonhandel – kunnskapsstatus og mulige konsekvenser». Formålet med denne utredningen har vært å belyse hvilke muligheter og begrensninger som ligger i salg av karbonkreditter for norske bønder samt å sammenstille og tilgjengeliggjøre informasjon om det frivillige karbonmarkedet og EUs frivillige rammeverk for sertifisering av karbonfjerning. Utredningen er finansiert av midler til klima- og miljøtiltak (KMP).