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3 results for “Castanea sativa (sweet chestnut)”
Potential and realized distribution at 30m for Sweet chestnut (Castanea sativa) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the sweet chestnut (<em>Castanea sativa, 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_castanea.sativa_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>castanea.sativa</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_castanea.sativa_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_castanea.sativa_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_castanea.sativa_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_castanea.sativa_<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>
Signatures of local adaptation to climate in natural populations of sweet chestnut (Castanea sativa Mill.) from southern Europe
Context: Temperate forest species, such as chestnut (Castanea sativa Mill.), are currently threatened by increasing temperature together with disruption and reduction of precipitation due to climate change. In this context, understanding the adaptation processes of species will help to manage and ensure the conservation of forests. Aims: We studied the relationship between genetic variability and climate variables in natural populations of C. sativa using a landscape genomics approach aimed to identify local adaption processes. Methods: Using five genomic SSRs and eight functional EST-SSRs markers, 268 individuals belonging to ten different natural European chestnut populations distributed in contrasting climatic sites were genotyped. In addition, associations between allelic variation and climatic variables (environmental association analyses approach) were performed using Samada and LFMM. Results: Results highlighted a strong inter-relationship between climate variables and evolutionary processes resulting in adaptive variation. STRUCTURE analysis based on functional markers split the populations in three separate gene pools (K=3), mostly in agreement with the different climatic conditions existing in the studied areas. Divergent spatial patterns of genetic variation between rainy and arid areas were found. We detected a total of 202 associations with climate among 22 different alleles, 9% of which related with the outlier locus FIR059, known to be implicated in regulatory mechanisms during water stress adaptation processes. Conclusions: Landscape genomics analyses revealed a pattern of adaptive variation, where specific climatic variables influenced the frequencies distribution and fixation of several alleles, resulting in local adaptation processes of the populations in the investigated areas. Our findings underline the close inter-relationship existing between climate and genetic variability, and indicate how this approach could provide valuable information for the management of forest species in a rapidly changing environment.
Gene flow between wild trees and cultivated varieties shapes the genetic structure of sweet chestnut (Castanea sativa Mill.) populations
<p>The sweet chestnut orchards (<em>Castanea sativa</em> Mill.) are traditionally planted in the northern Adriatic region. This study investigates their population structure, as well as the genetic background of three toponymous clonal varieties. Six genomic simple sequence repeat (gSSR) and nine EST-derived SSR (EST-SSR) loci were utilized in this study. We have identified five closely related clones, which represent a singular, polyclonal marron variety, found in all three cultivation areas, acompanied by many hybrids, resulting from the breeding between cultivated and wild chestnuts. </p>
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