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78 results for “Norway spruce”
X-ray scattering data from Norway spruce at different moisture conditions
<p>This data includes small and wide-angle X-ray scattering (SAXS, WAXS) intensities measured for Norway spruce (<em>Picea abies</em>) wood.</p> <p>The experiments were done in perpendicular transmission geometry, with the wood fiber axis roughly vertical and the radial direction of the wood tissue parallel to the X-ray beam, using a Xenocs Xeuss 3.0 C SAXS/WAXS device and Cu K-alpha radiation (wavelength 1.542 Å). The scattering patterns were recorded using an EIGER2 R 1M detector (pixel size 75 µm). The wood sample was measured first in wet state (saturated with water; "Wet"), and then equilibrated at different relative humidities (RH) in the following order: 95% ("RH95_1st"), 85% ("RH85"), 70% ("RH70"), 50% ("RH50_1st"), 20% ("RH20"), 10% ("RH10"), 50% ("RH50_2nd"), 95% ("RH95_2nd"). The sample-to-detector distance was 0.4139 m in SAXS, and 0.1528 m for the first 4 conditions (until "RH70") and 0.1525 m for the remaining 5 conditions in WAXS. Beam center (in detector pixels) was at x=540.6, y=667.0 (except y=635.0 in "RH70") in SAXS and x=1540, y=1521 in WAXS.</p> <p>For each of the 9 moisture conditions, files corresponding to 3 different processing steps are provided:</p> <ul> <li>"_bgsub_saxs.txt" and "_bgsub_waxs.txt" are ASCII files that contain the normalized and background-subtracted detector images (intensity in units mm^-1) corresponding to SAXS and WAXS, respectively. Pixels to be masked have the value "nan".</li> <li>"_bgsub_saxs_pol90.txt" and "_bgsub_waxs_pol90.txt" contain azimuthally regrouped images (90 bins in azimuthal angle) based on "_bgsub_saxs.txt" and "_bgsub_waxs.txt", respectively. PNG image files "_bgsub_saxs_pol.png" and "_bgsub_waxs_pol.png" are provided for reference.</li> <li>"_bgsub_pol90_ibg.txt" and "_bgsub_waxs_pol90_vert_ibg.txt" contain the equatorial and meridional anisotropic intensities, respectively, which were obtained from the azimuthally regrouped images by subtracting the isotropic scattering from the equatorial or meridional intensity (sector width 25°) at each value of the scattering vector <em>q</em>. The equatorial anisotropic intensities from SAXS and WAXS were merged by scaling the SAXS intensity, and the meridional anisotropic intensity is provided for the WAXS range only. The files contain columns for the magnitude of the scattering vector (q, unit Å<sup>-1</sup>), anisotropic intensity (I_ani, unit mm<sup>-1</sup>), error of anisotropic intensity (dI_ani, unit mm<sup>-1</sup>), and isotropic intensity (I_iso, unit mm<sup>-1</sup>).</li> </ul> <p>More detailed descriptions of the sample, the measurement, and the data processing can be found in the following reference:<br> Antti Paajanen, Aleksi Zitting, Lauri Rautkari, Jukka A. Ketoja, Paavo A. Penttilä. Nanoscale mechanism of moisture-induced swelling in wood microfibril bundles. <em>Nano Letters</em> 2022, 22(13): 5143–5150, DOI: 10.1021/acs.nanolett.2c00822</p>
Local adaptation to light in Norway spruce
<p>Exome capture data of the 1654 trees involved in the study of local adaptation to light quality in Norway spruce:</p> <p>1. control_genes.vcf - Raw vcf file of the ten control genes that were not differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden.</p> <p>2. degs.vcf - Raw vcf file of the 54 differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden, that showed at least one missense SNP in coding region. Missense variations in coding regions of nine candidate genes followed a latitudinal cline in allele and genotype frequencies.</p>
Potential and realized distribution at 30m for Norway spruce (Picea abies) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (<em>Picea abies, </em>L. H. Karst.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_picea.abies_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>picea.abies</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Global Cluster Test Results: Pathogenic Fungi in Decayed Norway Spruce Stands
<p><strong>Accessing the Results:</strong> Users can retrieve the test results by opening the dataset <code>Global_cluster_test_results.RData</code> in an R session and using the functions inside the script of the same name.</p> <p><strong>Description: </strong>This dataset provides global cluster test results analyzing the spatial distribution of pathogenic fungi in 273 Norway spruce stands in Norway (Lara et al., 2024). The stands, composed mainly of Norway spruce (27% to 100%), also include Scots pine and birch. It focuses on spatial patterns of decayed spruce trees, offering p-values, clustering metrics, and other parameters from statistical analyses.</p> <p><strong>Analysis:</strong> The dataset includes results from three global cluster tests (Tango, 2010):</p> <ul> <li>Tango's Nearest Neighbors (TNN)</li> <li>Tango's Double Exponential Clinal (TCN)</li> <li>Diggle and Chetwynd’s (DC)</li> </ul> <p><strong>Methodology:</strong> Cluster testing employed 1,000 Monte Carlo simulations for each test across all stands to establish null distributions and adjusted p-values, ensuring robust statistical assessments under the random labeling hypothesis: H0: the observed n0 decayed trees are a random sample from the entire sample of size n = n0 + n1 (decayed trees + healthy trees) (Tango, 2010).</p> <p> </p>
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Differential associations between nucleotide polymorphisms and physiological traits in Norway spruce (Picea abies Karst.) provenances under contrasting water regimes
<p>Three datasets are provided here, yielded by a study on drought-stressed and control (well-watered) seedlings of Norway spruce (Picea abies Karst.), coming from 5 provenances distributed along a steep altitudinal gradient from 550 to 1,280 m a.s.l. in central Slovakia:</p> <p>1. physiological traits</p> <p>2. double-digest restriction-site associated sequencing data (ddRAD)</p> <p>3. nuclear microsatellite (nSSR) genotypes</p>
Fig. 3 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 3. Seasonal dynamics of the average number of individuals per trap for the two species (± S. E.)
Fig. 1 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 1. Ordination (NMDS) of the pitfall catches based on the Bray-Curtis similarity index. ¡: Traps of the control plots and l: Traps of the leaf-litter plots
Fig. 1 in Damp Water Stream Impact For The Germination Of Norway Spruce (Picea Abies (L.) H. Karst.) Seeds
Fig. 1. Sowing scheme of Norway Spruce seeds (K – control sample – chemical treater was used for the seeds; 1s, 2s, 3s, 4s – damp water steam was used for the seeds).
An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)
<p>Data files for construction of the haploid genetic map for Norway spruce (<em>Picea abies</em>). Available at <a href="https://doi.org/10.1101/292151">https://doi.org/10.1101/292151</a></p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Data sets associated with the study for the Association mapping and identification of novel candidate loci affecting wood formation in Norway spruce</p>
Linked collectors and determiners for: Beetles from Old Spruce Forests in Southern Norway 2019.
Natural history specimen data linked to collectors and determiners held within, "Beetles from Old Spruce Forests in Southern Norway 2019". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b">https://bionomia.net/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b">https://gbif.org/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b</a>. Formatted as a Frictionless Data package.
Genomics of resistance in Norway spruce to Heterobasidion annosum s.s.and Heterobasidion parviporum
<p>Filtered variants and resistance traits to Heterobasidion annosum s.s. and Heterobasidion parviporum datasets for GWAS in Norway spruce</p>
Dendrometer and sapflow measurements of Norway spruce trees in a peatland harvesting experiment in Ränskälänkorpi
<p>Overview</p><p>This data set includes time series records of stem diameter variation and sap flow at breast height, soil water conditions and meteorology of/near eleven Norway spruce (<i>Picea abies</i>) trees at Ränskälänkorpi, Finland (61.2°N, 25.3°E). </p><p>The uploaded files contain cleaned observations of sap flow and stem radius, and all observations we processed to the same temporal resolution. </p><p>The data have been used in Liu <i>et al.</i> <i>Carbon source and sink limitations on boreal trees' cambial growth: implications of a coupled stomatal and growth model</i> (submitted to <i>New Phytologist</i> in October 2023). </p><p>Meanings (and units) of the columns</p><p><strong>Sheet "tree_stats" </strong></p><p> </p><p>Plot, CB = control block, SHB = selection harvest block.</p><p>DBH, diameter at breast height (cm)</p><p>SWdepth, sapwood depth (mm)</p><p>Wooddensity (kg m^{-3})</p><p>H, tree height (m)</p><p> </p><p><strong>The other sheets</strong></p><p> </p><p>DOY, day of the year</p><p>sapflow (litre per hour) measured using HPV-06 Implexx Sense.</p><p>SFD, sap flow density at breast height (mol H2O m^{-2} sapwood s^{-1}) converted from sapflow.</p><p>T_air, air temperature (°C)</p><p>RH, relative humidity (%)</p><p>VPD, vapour pressure deficit (Pa)</p><p>PPFD, photosynthetic photon flux density (mol m^{-2} s^{-1}), measured using [??? if you think worth mentioning].</p><p>Rain (mm)</p><p>D, VPD converted to mol H2O m^{-3} air using the ideal gas lawWTD, water table depth (cm) using Odyssey Capacitance Water loggers (Dataflow Systems Limited, Christchurch, New Zealand).</p><p>Phloem, reading of the point dendrometer (AX-5 Solartron Metrology) against the phloem (mm).</p>
Carpathian tree-ring network for European beech and Norway spruce
<p>Basic ecological theory suggests that a tradeoff between competitiveness and stress tolerance dictates species range limits at regional extents. However, empirical support for this key theory remains deficient because the necessary spatial and temporal coverage and scalability of field observations have rarely been achieved. We harnessed an extensive dendroecological network (>22,000 tree-ring samples from 816 forest inventory plots) to disentangle competition-limited from climate-limited growth in both overstory and understory trees. Growth synchrony among trees thereby served as an integral metric of climate sensitivity, an approach that we justify in supplementary analyses of growth responses to temperature, precipitation, and the standardized precipitation-evapotranspiration index. Sampling plots were arranged along elevational climate and vegetation gradients throughout the Carpathian Mountains, ranging from mixed-species lowland forests to coniferous forests at high elevations. With mixed-effect modelling, we also identified non-climatic factors (stand characteristics, species diversity, and disturbance history) that modulate spatial patterns in the growth rate and synchrony of European beech (<em>Fagus sylvatica</em> L.) and Norway spruce (<em>Picea abies</em> (L.) Karst.). Beech exhibited reduced growth and increased climate sensitivity towards higher elevations but performed better when species diversity was higher. The growth of spruce increased towards its lower range boundary, but understory cohorts grew poorly under interspecific competition. Overall, climate sensitivity was lower in more productive stands with benign climatic conditions and in recently disturbed sites with reduced stand density. These contrasting performances at mid-elevations where the two species overlap (900 – 1300 m a.s.l.) reflect their evolutionary history, which enables them to be competitive (beech) or cold-stress tolerant (spruce). This history will affect interactions between the two species under climate warming and shape macroecological patterns in the Carpathian ecoregion and likely other parts of Europe. Our findings point to a growing advantage of competitively stronger species in montane and subalpine vegetation zones.</p>
Norway spruce winrhizo and photosynthesis data
<p>Data conneted to the publication in Plant and Soil 2022 Genotypes exhibit no variation in precision foraging in mycorrhizal Norway spruce seedlings by Velmala Sannakajsa, Salmela Matti J., Chan Tommy, Hölttä Teemu, Hamberg Leena, Sievänen Risto, Pennanen Taina.</p>
TreeGrow - Data from: Morphology, bud burst and root fungal communities of Norway spruces (Picea abies)
<p>Our study delved into the relationship between root-associated fungi, gene expression and plant morphology in Norway spruce cuttings derived from both slow-and fast-growing trees. We found no clear link between the gene expression patterns of adventitious roots and the growth phenotype, suggesting no fundamental differences in the receptiveness to fungal symbionts between the phenotypes. Interestingly, saplings from slow-growing parental trees exhibited a higher richness of ectomycorrhizal species and larger roots. Some ectomycorrhizal species, typically found on mature spruces, were more prevalent on saplings from slow-growing spruces. The ericoid mycorrhizal fungus, Hyaloscypha hepaticola, showed a stronger association with saplings from fast-growing spruces. Moreover, saplings from slow-growing spruces had a greater number of Ascomycete taxa and free-living saprotrophic fungi. Aboveground sapling stems displayed some phenotypic variation; saplings from fast-growing phenotypes had longer branches but fewer whorls in their stems compared to those from the slow-growing group. In conclusion, the observed root-associated fungi and phenotypic characteristics in young Norway spruces may play a role in their long-term growth rate. This suggests that the early interactions between spruces and fungi could potentially influence their growth trajectory.</p>
Clinal variation in putative PAL/PTAL genes in Norway spruce
<p>Exome capture data of the 1654 trees involved in the detection of variation in putative PAL/PTAL genes in Norway spruce:</p> <p>pal_ptal_genes.vcf - Vcf file including the variations from the putative PAL/PTAL genes from the 1654 trees included in the six Norway spruce populations (S1-S6) from different latitudes across Sweden. Missense and synonymous variations in coding regions of PabPAL2, PabPTAL1 and PabPTAL3 followed a latitudinal cline in allele and genotype frequencies.</p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Genotypic data set for the association mapping in Norway spruce for wood formation and tracheid traits.</p>
Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce
<p>This dataset includes files and scripts used for the paper "Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce". </p>
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