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721 results for “ABI”
Identification of an altitudinal migration pattern of Abies pinsapo in the Baetic Mountains through the presence of its life stages
<p>This data set is used to explore the altitudinal shift of <em>Abies pinsapo</em> Boiss. in the Baetic System. We analysed the potential distribution of the realised and reproductive niches of <em>A. pinsapo</em> populations in the Ronda Mountains (Southern Spain) by using species distribution models (SDMs) for two life stages within the current populations. The realised and reproductive niches of <em>A. pinsapo</em> are different to one another, which may indicate a displacement in its altitudinal distribution.</p>
Potential and realized distribution at 30m for Silver fir (Abies alba) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the silver fir (<em>Abies alba, Mill.</em>) 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_abies.alba_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>abies.alba</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_abies.alba_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_abies.alba_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_abies.alba_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_abies.alba_<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_tree.species_anv.pnv.eml">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>
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>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
Herbarium specimen image of Picea abies (L.) H.Karst., part of the collection of Meise Botanic Garden
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Abies grandis Lindl., part of the collection of Meise Botanic Garden
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Geographical gradients of genetic diversity and differentiation among the southernmost marginal populations of Abies sachalinensis revealed by EST-SSR polymorphism
Research Highlights: We detected the longitudinal gradients of genetic diversity parameters, such as the number of alleles, effective number of alleles, heterozygosity, and inbreeding coefficient, and found that these might be attributable to climatic conditions, such as temperature and snow depth. Background and Objectives: Genetic diversity among local populations of a plant species at its distributional margin has long been of interest in ecological genetics. Populations at the distribution center grow well in favorable conditions, but those at the range margins are exposed to unfavorable environments, and the environmental conditions at establishment sites might reflect the genetic diversity of local populations. This is known as the central-marginal hypothesis in which marginal populations show lower genetic variation and higher differentiation than do central populations. In addition, genetic variation in a local population is influenced by phylogenetic constraints and the population history of selection under environmental constraints. In this study, we investigated this hypothesis in relation to Abies sachalinensis, a major conifer species in Hokkaido. Materials and methods: A total of 1,189 trees from 25 natural populations were analyzed using 19 EST-SSR loci. Results: The eastern populations; namely, those in the species distribution center, showed greater genetic diversity than did the western peripheral populations. Another important finding is that the southwestern marginal populations were highly differentiated from the other populations. Conclusions: These differences might be due to genetic drift in the small and isolated populations at the range margin. Therefore, our results indicated that the central-marginal hypothesis held true for the southernmost A. sachalinensis populations in Hokkaido.
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>
Abies magnifica (Pinaceae) - leaf - showing orientation on twig
Image of Abies magnifica (Pinaceae) - leaf - showing orientation on twig
Abies magnifica (Pinaceae) - cone - female - closed
Image of Abies magnifica (Pinaceae) - cone - female - closed
Abies fraseri (Pinaceae) - cone - female - closed
Image of Abies fraseri (Pinaceae) - cone - female - closed
Abies fraseri (Pinaceae) - whole tree - general
Image of Abies fraseri (Pinaceae) - whole tree - general
Abies fraseri (Pinaceae) - cone - male
Image of Abies fraseri (Pinaceae) - cone - male
Abies fraseri (Pinaceae) - leaf - showing orientation on twig
Image of Abies fraseri (Pinaceae) - leaf - showing orientation on twig
Abies fraseri (Pinaceae) - leaf - entire needle
Image of Abies fraseri (Pinaceae) - leaf - entire needle
Abies fraseri (Pinaceae) - twig - showing attachment of needles
Image of Abies fraseri (Pinaceae) - twig - showing attachment of needles
Abies fraseri (Pinaceae) - leaf - showing orientation on twig
Image of Abies fraseri (Pinaceae) - leaf - showing orientation on twig
Abies fraseri (Pinaceae) - whole tree - general
Image of Abies fraseri (Pinaceae) - whole tree - general
Abies fraseri (Pinaceae) - bark - of a large tree
Image of Abies fraseri (Pinaceae) - bark - of a large tree
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