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Sorbus aucuparia

ABSTRACT: Sorbus aucuparia Raw sequence reads

PROVIDER: PRJNA990467 | ENA |

REPOSITORIES: ENA

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Dataset's files

Source:

			Action	DRS
	SRR25118500_1.fastq.gz	Fastqsanger.gz
	SRR25118500_2.fastq.gz	Fastqsanger.gz
	SRR25118501_1.fastq.gz	Fastqsanger.gz
	SRR25118501_2.fastq.gz	Fastqsanger.gz
	SRR25118502_1.fastq.gz	Fastqsanger.gz

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Genetic Diversity and Structure of Rear Edge Populations of <i>Sorbus aucuparia</i> (Rosaceae) in the Hyrcanian Forest.

Project description:Sorbus aucuparia (Rosaceae) is a small tree species widely distributed in Eurasia. The Hyrcanian forest is the southernmost distribution limit of this species. Severe habitat degradation and inadequate human interventions have endangered the long-term survival of this species in this region, and it is necessary to develop and apply appropriate management methods to prevent the loss of its genetic diversity. In this study, we used 10 SSR markers in order to evaluate the genetic diversity of this taxon. Leaf samples were collected from five known populations of S. aucuparia throughout its distribution area in the Hyrcanian forest. Expected heterozygosity ranged from 0.61 (ASH) to 0.73, and according to the M-ratio, all populations showed a significant reduction in effective population size, indicating a genetic bottleneck. Global FST was not statistically significant and attained the same values with and without excluding null alleles (ENA) correction (FST = 0.12). Bayesian analysis performed with STRUCTURE defined two genetic clusters among the five known populations, while the results of discriminant analysis of principal components (DAPC) identified three distinct groups. The average proportion of migrants was 22. In general, the gene flow was asymmetrical, with the biggest differences between immigration and emigration in Barzekoh and Asbehriseh. The Mantel test showed that there was no significant correlation between genetic distance (FST) and geographic distance in S. aucuparia. The best pathway for theoretical gene flow is located across the coast of the Caspian Sea and significant spatial autocorrelation was observed in only one population. In order to reduce the extinction risk of very small and scattered populations of S. aucuparia in the Hyrcanian forest, it is very important to establish and/or enhance the connectivity through habitat restoration or genetic exchange.

| S-EPMC8309350 | biostudies-literature

Competitive success of southern populations of Betula pendula and Sorbus aucuparia under simulated southern climate experiment in the subarctic.

Project description:Global warming has been commonly accepted to facilitate species' range shifts across latitudes. Cross-latitudinal transplantations support this; many tree species can well adapt to new geographical areas. However, these studies fail to capture species' adaptations to new light environment because the experiments were not designed to explicitly separate species' responses to light and temperature. Here we tested reaction norms of tree seedlings in reciprocal transplantations 1,000 km apart from each other at two latitudes (60°N and 69°N). In contrast to past studies, we exposed our experimental plants to same temperature in both sites (temperature of 60°N growing site is recorded to adjust temperature of 69°N site in real time via Internet connection) while light environment (photoperiod, light quality) remained ambient. Shoot elongation and autumn coloration were studied in seedlings of two deciduous trees (Betula pendula and Sorbus aucuparia), which were expected to respond differently to day length. Sorbus as a member of Rosaceae family was assumed to be indifferent to photoperiod, while Betula responds strongly to day length. We hypothesized that (1) southern and northern populations of both species perform differently; (2) southern populations perform better in both sites; (3) autumn phenology of southern populations may delay in the northern site; (4) and Sorbus aucuparia is less dependent on light environment. According to the hypotheses, shoot elongation of northern population was inherently low in both species. An evolutionary consequence of this may be a competitive success of southern populations under warming climate. Southern population of B. pendula was delayed in autumn coloration, but not in growth cessation. Sorbus aucuparia was less responsive to light environment. The results suggest that light provides selection pressure in range shifts, but the response is species dependent.

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EMARaV in Sorbus intermedia

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Patterns of evolution at the gametophytic self-incompatibility Sorbus aucuparia (Pyrinae) S pollen genes support the non-self recognition by multiple factors model.

Project description:S-RNase-based gametophytic self-incompatibility evolved once before the split of the Asteridae and Rosidae. In Prunus (tribe Amygdaloideae of Rosaceae), the self-incompatibility S-pollen is a single F-box gene that presents the expected evolutionary signatures. In Malus and Pyrus (subtribe Pyrinae of Rosaceae), however, clusters of F-box genes (called SFBBs) have been described that are expressed in pollen only and are linked to the S-RNase gene. Although polymorphic, SFBB genes present levels of diversity lower than those of the S-RNase gene. They have been suggested as putative S-pollen genes, in a system of non-self recognition by multiple factors. Subsets of allelic products of the different SFBB genes interact with non-self S-RNases, marking them for degradation, and allowing compatible pollinations. This study performed a detailed characterization of SFBB genes in Sorbus aucuparia (Pyrinae) to address three predictions of the non-self recognition by multiple factors model. As predicted, the number of SFBB genes was large to account for the many S-RNase specificities. Secondly, like the S-RNase gene, the SFBB genes were old. Thirdly, amino acids under positive selection-those that could be involved in specificity determination-were identified when intra-haplotype SFBB genes were analysed using codon models. Overall, the findings reported here support the non-self recognition by multiple factors model.

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