Multiple Approach for Plant Biodiversity Conservation in Restoring Forests

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Monumental chestnut trees: source of genetic diversity, cultural and landscape value

The monumental trees are unique individuals of venerable age and considerable size, which represent a heritage of inestimable historical, cultural, landscape, and scientific value for the territtory. They also constitute a source of genetic diversity which confers them longevity and ability to adapt to climate and environmental changes. In this context, studies on centennial trees can be useful for interpretatiton of species history as migration events, selection and anthropogenic actiton. The aim of this research was to evaluate the genetic variability of ancient Castanea sativa trees and relate them to actual natural/naturalized populatitons and varieties in order to enhance our knowledge about the demography, cultivatiton processes and the impact of these giant trees on the genetic diversity of the species. We selected a total of 182 ancient trees from Spain and Central - Southern Italy. For each tree, more than one sample was collected to test for genetic integrity and grafing. The samples were genotyped by means of nuclear microsatellite markers and the variability of plastid DNA regitons (trnH-psbA and trnK/matK) was also tested. Using the sofware GeneALex and HPrare, we evaluated observed (Hto) and expected (He) heterozygosity, allelic richness (Ar) private allelic richness (pAr). A Bayesian analysis was performed using the sofware STRUCTURE to identify the different gene pools and gentotypes. The obtained genetic data were compared with those of natural populations and cultivars collected in the same geographic areas. Higher values of allelic richness were observed in the ancient chestnut trees, a genetic similarity of these individual trees to the natural populations was highlighted. A phylogetographic structure of plastid diversity was alsto established. Eleven genotypes were coincident with 11 cultivars in the EU database. Based on the putative age of giant trees we can hyptothesize that the grafing practice occurred in the Iberian peninsula in the 15th century and in the 17th century in Italy. This work provides new knowledge about the history and domesticatiton tof European chestnut, the results are relevant for the conservatiton and management of Castanea sativa genetic resources

Comparative systematics and phylogeography of Quercus Section Cerris in western Eurasia: inferences from plastid and nuclear DNA variation

Oaks (Quercus) comprise more than 400 species worldwide and centres of diversity for most sections lie in the Americas and East/Southeast Asia. The only exception is the Eurasian sect. Cerris that comprises about 15 species, most of which are confined to western Eurasia. This section has not been comprehensively studied using molecular tools. Here, we assess species diversity and provide a first comprehensive taxonomic and phylogeographic scheme of western Eurasian members of sect. Cerris using plastid (trnH-psbA) and nuclear (5S-IGS) DNA variation with a dense intra-specific and geographic sampling. Chloroplast haplotypes primarily reflected phylogeographic patterns originating from interspecific cytoplasmic gene flow within sect. Cerris and its sister section Ilex. We identified two widespread and ancestral haplotypes, and locally restricted derived variants. Signatures shared with Mediterranean species of sect. Ilex, but not with the East Asian Cerris oaks, suggest that the western Eurasian lineage came into contact with Ilex only after the first (early Oligocene) members of sect. Cerris in Northeast Asia had begun to radiate and move westwards. Nuclear 5S-IGS diversification patterns were more useful for establishing a molecular-taxonomic framework and to reveal hybridization and reticulation. Four main evolutionary lineages were identified. The first lineage is comprised of Q. libani, Q. trojana and Q. afares and appears to be closest to the root of sect. Cerris. These taxa are morphologically most similar to the East Asian species of Cerris, and to both Oligocene and Miocene fossils of East Asia and Miocene fossils of western Eurasia. The second lineage is mainly composed of the widespread Q. cerris and the narrow endemic species Q. castaneifolia, Q. look, and Q. euboica. The third lineage comprises three Near East species (Q. brantii, Q. ithaburensis and Q. macrolepis), well adapted to continental climates with cold winters. The forth lineage appears to be the most derived and comprises Q. suber and Q. crenata. Q. cerris and Q.  trojana displayed high levels of variation; Q. macrolepis and Q. euboica, previously treated as subspecies of Q. ithaburensis and Q. trojana, likely deserve independent species status. A trend towards inter-specific crosses was detected in several taxa; however, we found no clear evidence of a hybrid origin of Q. afares and Q. crenata, as currently assumed

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches