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12 results for “Pinus pinea”
A DETAILED TIME SERIES OF HOURLY CIRCUMFERENCE VARIATIONS IN PINUS PINEA L. IN CHILE
<ul> <li>The dataset provides digital dendrometer measurements on stem circumference of irrigated and non-irrigated<em> Pinus pinea</em> trees. Data were obtained in a xeric non-native habitat of central Chile. Forest mensuration were hourly collected from six adult trees during a growth year.</li> </ul>
Potential and realized distribution at 30m for Stone pine (Pinus pinea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the stone pine (<em>Pinus pinea</em><em>, L.</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_pinus.pinea_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>pinus.pinea</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_pinus.pinea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.pinea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.pinea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.pinea_<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>
Geographic variation of tree height of Pinus pinea L. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinea </em>L. planted in common gardens in France and Spain, between years 1993 and 1997. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimensions of this database is 56,624 individual tree height measurements <em> </em>with 9 common gardens and 55 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Pinus pinea L. (BR0000009237575)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Pinus pinea cone, seed and pine nut morphometry and health
<p>Seven plantations were monitored in winters of the 2010-2020 period, except for 2016. Ten healthy 3-year-old cones were randomly harvested per plantation each year, following former study [28]; in few years the number of collected cones was lower than 10 due to harvesting complexities. Cones were collected from different trees and immediately weighed (fresh weight), as indicated by several authors. Harvested cones were processed at INFOR’s laboratory to extract seeds (in-shell pine nuts) and pine nuts (kernels) each year, totaling 560 cones in the study period. Seed number per cone was counted and seed and pine nuts were weighed and measured in the laboratory. Empty and damaged seeds were also quantified to monitor cone health.</p>
Transects in Pinus pinea regeneration - Sardegna
<p>Regeneration of Pinus pinea on a sandy beach east cost of Sardegna, Italy</p>
Fig. 4 in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 4. Relation of the number of wounded Pinus pinaster trees, after Monochamus galloprovincialis feeding, the average number of wounds per tree, and wound length and width. Bars: standard error.
Fig. 3 in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 3. Changes in the profile of volatiles released by Pinus pinea trees being fed on by Monochamus galloprovincialis adults. Bars: standard error. Letters in the table in the right side of the graph represent ANOVA post-hoc Fisher's Least Significant Difference test Homogenous Groups, (1): All volatiles: F(8,90) = 7.88; p <0.0001***; (2) Without limonene: F(7,80) = 2.31; p = 0.034*.
Fig. 6. A. Pinus pinaster individuals. B in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 6. A. Pinus pinaster individuals. B. SPME collection of P. pinaster volatiles in control experiments. C. SPME collection of P. pinaster volatiles during Monochamus galloprovincialis feeding. D. Detail of M. galloprovincialis inside the net. E. M. galloprovincialis feeding on P. pinaster. F. Injured tree trunk. G. Oleoresin being exuded from the tree trunk.
Fig. 5 in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 5. Relation of the number of wounded Pinus pinea trees, after Monochamus galloprovincialis feeding, the average number of wounds per tree, and wound length and width. Bars: standard error.
Fig. 2 in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 2. Changes in the profile of volatiles released by Pinus pinaster trees being fed on by Monochamus galloprovincialis adults, with trees grouped according to their essential oil chemotypes: β-pinene, α-pinene, and δ-3-carene EO dominance (chemotype 1, C1 in dark grey) and δ-3-carene only in trace amounts (chemotype 2, C2 in white). Bars: standard error. Letters in the table on the right side of the graph represent ANOVA post-hoc Fisher's Least Significant Difference test Homogenous Groups; Chemotype 1 (C1): F(16,170) = 1.58; p = 0.078*; Chemotype 2 (C2): F(16,442) = 4.04; p <0.0001***.
Fig. 1 in Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles
Fig. 1. Changes in the profile of volatiles released by all Pinus pinaster trees being fed on by Monochamus galloprovincialis adults. Bars: standard error. Letters in the table on the right side of the graph represent ANOVA post-hoc Fisher's Least Significant Difference test Homogenous Groups; (1) ANOVA with all volatiles: F(16,629) = 4.07; p <0.0001***; (2) ANOVA without α-pinene and β-pinene: F(14,555) = 4.63; p
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