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.
2024
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Trygve S. Aamlid Geir Kjølberg Knudsen Paula Izabella Lawicka Trond Olav Pettersen Victoria Stornes MoenSammendrag
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Lars T. HavstadSammendrag
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Darius KviklysSammendrag
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Dylan Grobler Juliana D. Klein Matthew L. Dicken Kolobe L, Mmonwa Michelle Soekoe Michaela van Staden Snorre Hagen Simo Maduna Aletta E. Bester-van der MerweSammendrag
Globally, hammerhead sharks have experienced severe declines owing to continued overexploitation and anthropogenic change. The smooth hammerhead shark Sphyrna zygaena remains understudied compared to other members of the family Sphyrnidae. Despite its vulnerable status, a comprehensive understanding of its genetic landscape remains lacking in many regions worldwide. The present study aimed to conduct a fine-scale genomic assessment of Sphyrna zygaena within the highly dynamic marine environment of South Africa's coastline, using thousands of single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (3RAD). A combination of differentiation-based outlier detection methods and genotype-environment association (GEA) analysis was employed in Sphyrna zygaena. Subsequent assessments of putatively adaptive loci revealed a distinctive south to east genetic cline. Among these, notable correlations between adaptive variation and sea-surface dissolved oxygen and salinity were evident. Conversely, analysis of 111,243 neutral SNP markers revealed a lack of regional population differentiation, a finding that remained consistent across various analytical approaches. These results provide evidence for the presence of differential selection pressures within a limited spatial range, despite high gene flow implied by the selectively neutral dataset. This study offers notable insights regarding the potential impacts of genomic variation in response to fluctuating environmental conditions in the circumglobally distributed Sphyrna zygaena.
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Aim Seedling recruitment is a vital process for forest regeneration and is influenced by various factors such as stand composition, climate, and soil disturbance. We conducted a long-term field experiment (18 years) to study the effects of these factors and their interactions on seedling recruitment. Location Our study focused on five main species in boreal mixed woods of eastern Canada: trembling aspen (Populus tremuloides), paper birch (Betula papyrifera), white spruce (Picea glauca), balsam fir (Abies balsamea), and white cedar (Thuja occidentalis). Methods Sixteen 1-m2 seedling monitoring subplots were set up in each of seven stands originating from different wildfires (fire years ranging from 1760 to 1944), with a soil scarification treatment applied to every other subplot. Annual new seedling counts were related to growing-season climate (mean temperature, growing degree days and drought code), scarification, and stand effects via a Bayesian generalized linear mixed model. Results Soil scarification had a large positive effect on seedling recruitment for three species (aspen, birch and spruce). As expected, high mean temperatures during the seed production period (two years prior to seedling emergence) increased seedling recruitment for all species but aspen. Contrary to other studies, we did not find a positive effect of dry conditions during the seed production period. Furthermore, high values of growing degree days suppressed conifer seedling recruitment. Except for white cedar, basal area was weakly correlated with seedling abundance, suggesting a small number of reproductive individuals is sufficient to saturate seedling recruitment. Conclusion Our findings underscore the importance of considering multiple factors, such as soil disturbance, climate, and stand composition, as well as their effects on different life stages when developing effective forest management strategies to promote regeneration in boreal mixed-wood ecosystems.
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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.
Forfattere
Nina SvartedalSammendrag
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