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Plant diversity is generally thought to enhance productivity, driven by either (1) chance inclusion of highly productive species in more diverse communities or (2) niche-based resource acquisition with competitive interactions increasing resource use efficiency. Here, we ask whether weeding, as employed in most experiments to date, might contribute to the positive diversity-productivity relationship reported for many grasslands. Using all 82 species from our local pool, we constructed 357 experimental grassland plots (2 × 4 m each), arranged as a completely randomized experiment in an arable field prepared to minimize existing seed bank. The plots were sown to vary species richness (1, 2, 4, 6, 8, 10, 15, 20, 25, 30, 35 or 40 species) and were maintained under both ambient conditions and experimental drought. A single monoculture plot was maintained for all 82 species, and each of the other eleven species richness levels was replicated 25 times. Plots were maintained strictly without weeding, and aboveground biomass was measured at 17, 19, 27 and 29 months after the start of this experiment. No single measure of biodiversity was significantly correlated with productivity consistently across all four sampling periods. Furthermore, there were only weak overall effects of six biodiversity variables (the species richness planted, observed, and sampled; Shannon diversity, effective species richness and evenness in the sampled area) on productivity under either precipitation treatment. Regression analysis identified no equation that used a consistent subset of the biodiversity measures as predictors. In view of these transient and insubstantial effects, results from previous experiments that employed weeding treatments are suspect as tests of the hypothesis that biodiversity has positive effects on productivity.
The sequences of the internal transcribed spacer (ITS) regions of ectomycorrhizal fungi collected from Sichuan Province were analyzed using a PCR primer pair specific to T. matsutake. The amplified fragments were sequenced and compared with each other to build a phylogenetic tree. The mRNA deep sequencing approach was adopted to identify differentially expressed T. matsutake genes among the transcriptomes developed from a Xiaojin sample. A phylogenetic analysis of the aligned sequences was performed using maximum-likelihood (ML) and neighbor-joining (NJ) analyses. The results clearly showed that the KD (KM657344) and BT (KM657342) strains were more closely related to each other than to other strains. Moreover, T. matsutake from Sichuan differed from those specimens derived from Heilongjiang, Yunnam, and Guizhou provinces of China, Finland, and Japan. Furthermore, there was extremely high homology among these T. matsutake samples, despite some genetic variation. In addition, the genome of T. matsutake was sequenced using Illumina sequencing technology (RNA-seq). In all, a total of 24,549,990 reads were obtained that yielded 18,266,492 high-quality clean reads. The quality reads were excluded later. The BLAST analysis of the sequence reads against the NR database indicated that T. matsutake shared a high number of contigs with Laccaria bicolor. The results also indicated that catalytic activity, metabolic processes, metabolic pathways, and biosynthesis of secondary metabolites were the main functions identified by gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Phylogenetic analysis showed that T. matsutake growing in Sichuan differed from samples growing in other regions. The differences in secondary metabolites between the Sichuan and Xiaojin samples may be due to differences in metabolic pathways. Thus the study provides a foundation for understanding T. matsutake biogeography and origins, and identifies DEGs in the Xiaojin sample to help elucidate the molecular mechanisms in secondary metabolite synthesis.
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