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20 results for “Pinus halepensis”
Potential and realized distribution at 30m for Aleppo pine (Pinus halepensis) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Aleppo pine (<em>Pinus halepensis, 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_pinus.halepensis_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.halepensis</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.halepensis_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.halepensis_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.halepensis_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.halepensis_<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="http://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read</strong> the tutorial with executable code on our <a href="http://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="http://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>
Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in <em>Pinus halepensis</em> Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Parameters included in the dataset: <br> Plot: plot identification in the SNFI and iuFOR networks.<br> Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis)<br> Slope: gradient in the plot in percentage.<br> Altitude: elevation of the plot in meters above the sea level<br> Latitude and Longitude: geographical coordinates of the plots in degrees<br> Density: number of trees per hectare in the plot<br> Dg: quadratic mean diameter in centimetersç<br> Hm: mean height in meters of the trees in the plot<br> H0; dominant height in meters of the trees in the plot<br> BA: basal area of the plot in square meters per hectare<br> SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands) <br> SQ: the site quality class<br> Age: average age in years of the trees in the plot<br> AW: soil available water in percentage<br> CO: soil coarse particles in percentage<br> Porosity: soil porosity in percentage<br> CLAY: clay content in soil in percentage<br> SILTUS: silt content in soil following the USDA criteria in percentage<br> SILTIS: silt content in soil following the International criteria, in percentage<br> SANDUS: sand content in soil following the USDA criteria, in percentage<br> SANDIS: sand content in soil following the International criteria, in percentage<br> OHT: organic horizon thickness in the plot in centimeters<br> ([C/N]L): the total carbon to total nitrogen ratio in the litter fraction of the organic horizon<br> ([C/N]FH): the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon <br> L: amount of litter fraction in the organic horizon in tons per hectare<br> FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare. <br> pH: soil pH value <br> CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934)<br> AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962)<br> TN: total N in soil in percentage<br> TOC/TN: total organic C to total N ratio in soil<br> Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945)<br> WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983)<br> Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972)<br> React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007)<br> Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954)<br> Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978)<br> EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> Sat: base saturation of soil in percentage <br> AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968)<br> AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987) <br> AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996)<br> AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971)<br> Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987)<br> Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952)<br> Cmin/TOC: mineralizable carbon to total organic carbon ratio <br> Cmic/TOC: microbial biomass carbon to total organic carbon ratio<br> qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil<br> FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute)<br> DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute)<br> AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute)<br> Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute)<br> Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O<sub>2</sub> produced per gram of soil and minute)<br> MAT: mean annual temperature in degrees centigrade (Ninyerola et al., 2005)<br> MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MMCM: mean maximum temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MTCM: mean temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> TP: total precipitation in millimeters (Ninyerola et al., 2005)<br> PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005)<br> PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> AHI: Annual Hydric Index (Thornthwaite, 1949)<br> Martonne: Martonne index (De-Martonne, 1926)<br> Lang: Lang index (Lang, 1919)</p> <p>Code -999.99 indicates missing values.</p>
Pinus halepensis M.Bieb. (BR0000025052206)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Does recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area?
<p>Data used for analyses in the paper by Romero B. and Ganteaume A., published in Annals of Forest Science in 2020.</p> <p>PS : <em>Pinus sylvestris</em></p> <p>PH : <em>Pinus halepensis</em></p>
Dataset of site factors in Pinus halepensis Mill. plantations in Spain
<p>This dataset contains information about soil, climatic, physiographic and stand parameters of 32 plots located in <em>Pinus halepensis</em> Mill. plantations in Spain. </p>
Dataset of soil, climatic and stand variables in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>Soil, climatic and stand variables measured in 35 <em>Pinus sylvestris</em> and 32 <em>Pinus halepensis </em>plantations in Castilla y León region (Spain)</p>
Data from: First insights into the transcriptome and development of new genomic tools of a widespread circum-Mediterranean tree species, Pinus halepensis Mill.
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Data from: Allelopathic effects of volatile organic compounds released from Pinus halepensis needles and roots
The Mediterranean region is recognized as a global biodiversity hotspot. However, over the last decades, the cessation of traditional farming in the north part of Mediterranean basin has given way to strong afforestation leading to occurrence of abandoned agricultural lands colonized by pioneer expansionist species like Pinus halepensis. This pine species is known to synthesize a wide range of secondary metabolites and previous studies have demonstrated strong allelopathic potentialities of its needle and root leachates. Pinus halepensis is also recognized to release significant amounts of volatile organic compounds (VOC) with potential allelopathic effects that has never been investigated. In this context, the objectives of the present study were to improve our knowledge about the VOC released from P. halepensis needles and roots, determine if these VOC affect the seed germination and root growth of two herbaceous target species (Lactuca sativa and Linum strictum), and evaluate if soil microorganisms modulate the potential allelopathic effects of these VOC. Thirty terpenes were detected from both needle and root emissions with β-caryophyllene as the major volatile. Numerous terpenes, such as β-caryophyllene, -terpinene or -pinene showed higher headspace concentrations according to the gradient green needles < senescent needles < needle litter. Seed germination and root growth of the two target species were mainly reduced in presence of P. halepensis VOC. In strong contrast with the trend reported with needle leachates in literature, we observed an increasing inhibitory effect of P. halepensis VOC with the progress of needle physiological stages (i.e. green needle < senescent needle < needle litter). Surprisingly, several inhibitory effects observed on filter paper were also found or even amplified when natural soil was used as a substrate, highlighting that soil microorganisms do not necessarily limit the negative effects of VOC released by P. halepensis on herbaceous target species.
Data from: Fire-induced population reduction and landscape opening increases gene flow via pollen dispersal in Pinus halepensis
Population reduction and disturbances may alter dispersal, mating patterns and gene flow. Rather than taking the common approach of comparing different populations or sites, here we studied gene flow via wind-mediated effective pollen dispersal on the same plant individuals before and after a fire-induced population drop, in a natural stand of Pinus halepensis. The fire killed 96% of the pine trees in the stand and cleared the vegetation in the area. Thirteen trees survived in two groups separated by ~80 m, and seven of these trees had serotinous (closed) pre-fire cones that did not open despite the fire. We analyzed pollen from closed pre- and post-fire cones using microsatellites. The two groups of surviving trees were highly genetically differentiated, and the pollen they produced also showed strong among-group differentiation and very high kinship both before and after the fire, indicating limited and very local pollen dispersal. The pollen not produced by the survivors also showed significant pre-fire spatial genetic structure and high kinship, indicating mainly within-population origin and limited gene flow from outside, but became spatially homogeneous with random kinship after the fire. We suggest that post-fire gene flow via wind-mediated pollen dispersal increased by two putative mechanisms: 1) a drastic reduction in local pollen production due to population thinning, effectively increasing pollen immigration; 2) an increase in wind speeds in the vegetation-free post-fire landscape. This research shows that dispersal can alleviate negative genetic effects of population size reduction, and that disturbances might enhance gene flow, rather than reduce it.
Data from: Genetic structure of a naturally regenerating post-fire seedling population: Pinus halepensis as a case study
To study the effects of wildfire on population genetics of a wind pollinated and wind dispersed tree, we have analyzed the genetic structure of a post-fire, naturally regenerating seedling population of Pinus halepensis Miller, on Mt. Carmel, Israel. We tested the existence of spatial genetic structure, which is expected due to the special spatial demographic structure of the post-fire seedling and sapling populations of this species. Explicitly, we asked whether or not seedlings that germinated under large, burned, dead pine trees are also their offspring. The results revealed that the post-fire seedling population is polymorphic, diverse, and reflects the pre-fire random mating system. In contrast to our prediction, we found no division of the post-fire seedling population to distinct sub-populations. Furthermore, as a result of post-fire seed dispersal to longer range than the average pre-fire inter-tree distance, seedlings found under individual burned trees were not necessarily their sole offspring. Although the population as a whole showed a Hardy-Weinberg equilibrium, significant excess of heterozygotes was found within each tallest seedlings group growing under single, large, burned pine trees. Our finding indicates the possible existence of intense natural selection for the most vigorous heterozygous genotypes that are best adapted to the special post-fire regeneration niche, which is the thick ash bed under large, dead, pine trees.
Environmental and serotiny data of Pinus halepensis
<p><span>Many plants undergo adaptation to fire. Yet, as global change is increasing fire frequency worldwide, our understanding of the genetics of adaptation to fire is still limited. </span><span>We studied the genetic basis of serotiny (the ability to disseminate seed exclusively after fire) in the widespread, pioneer Mediterranean conifer <em>Pinus halepensis</em> Mill., by linking individual variation in serotiny presence and level to fire frequency and to genetic polymorphism in natural populations. </span></p> <p><span>After filtering steps, 885 Single Nucleotide Polymorphisms (SNPs) out of 8,000 SNPs used for genotyping were implemented to perform an <em>in situ</em> association study between genotypes and serotiny presence and level. To identify serotiny-associated loci, we performed random forest analyses of the effect of SNPs on serotiny levels, while controlling for tree size, frequency of wildfires, and background environmental parameters.</span></p> <p><span>Serotiny showed a bimodal distribution, with serotinous trees more frequent in populations exposed to fire in their recent history. </span><span>Twenty-two</span><span> SNPs found in genes involved in stress tolerance were associated with presence-absence of serotiny while thirty-seven found in genes controlling for flowering were associated with continuous serotiny variation.</span></p> <p><span>This study </span><span>shows</span><span> the high potential of </span><em><span>P. halepensis</span></em><span> to adapt to changing fire regimes, benefiting from a large and flexibl</span><span>e</span> <span>genetic basis of trait variation</span><span>.</span></p>
Ajustements de tarifs de cubage du bois fort pour la gestion de pin d'Alep (Pinus halepensis Mill.) dans la forêt de Beni Imloul en Algérie
<pre> </pre> <p>This file includes the Aleppo pine dataset used for the development of single and double-entry volume tables in the massif of Beni Imloul, Algeria. </p> <p>The data cited are: diameter in (cm), total height in (m) and volume in (m<sup>3</sup>). </p>
Environmental and serotiny data of Pinus halepensis
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Data from: Fire-induced population reduction and landscape opening increases gene flow via pollen dispersal in Pinus halepensis
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Data from: Genetic structure of a naturally regenerating post-fire seedling population: Pinus halepensis as a case study
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Data from: Genetic evidence for a Janzen-Connell recruitment pattern in reproductive offspring of Pinus halepensis trees.
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Data from: Inferring selection in instances of long‐range colonization: the Aleppo pine (Pinus halepensis) in the Mediterranean Basin
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Data from: Allelopathic effects of volatile organic compounds released from Pinus halepensis needles and roots
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Looking for local adaptation: convergent microevolution in Aleppo pine (Pinus halepensis).
<p>SNP dataset in Aleppo pine (<em>Pinus halepensis</em>) published in <em>Genes</em> <strong>2019</strong>, <em>10</em>(9), 673; https://doi.org/10.3390/genes10090673</p>
Pinus halepensis_Community Matrix
<p>The initial document comprises the community matrix dataset, encompassing plant co-occurrences and abundances within pinewood-burned areas. This data was gathered through the strategic placement of 10 permanent plots, each spanning 100 m2. These plots were systematically positioned at a minimum distance of 100 m, ensuring they were situated 50 meters away from the periphery of the burned areas. For one year, from late winter to early autumn in 2022, we conducted four distinct sampling sessions, each occurring bi-monthly.</p><p>As for the second file, it encompasses various traits pertinent to the study of each species.</p>
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