Nutrient inputs to forest ecosystems significantly influence aboveground plant community structure and ecosystem functioning. However, our knowledge of the influence of nitrogen (N) and/or phosphorus (P) inputs on belowground microbial communities in subtropical forests is still unclear. In this study, we used quantitative polymerase chain reaction and Illumina Miseq sequencing of the bacterial 16S rRNA gene to investigate bacterial abundance, diversity, and community composition in a Chinese fir plantation. The fertilization regimes were as follows: untreated control (CK), P amendment (P), N amendment (N), and N with P amendment (NP). Additions of N decreased soil pH and bacterial 16S rRNA gene abundance by 3.95 (from 4.69 to 3.95) and 3.95 × 109 copies g−1 dry soil (from 9.27 × 109 to 3.95 × 109 g−1 dry soil), respectively. Bacterial richness and diversity decreased with N addition (N and NP) rather than only P input. Proteobacteria, Acidobacteria, and Actinobacteria were the major phylum across all treatments. Nitrogen addition increased the relative abundance of Proteobacteria and Actinobacteria by 42.0 and 10.5%, respectively, while it reduced that of Acidobacteria by 26.5%. Bacterial community structure in the CK and P treatments was different from that in the N and NP treatments upon principle coordinates analysis. Phosphorus addition did not significantly affect soil bacterial communities, and no interactions between N and P inputs on microbial traits were observed. Soil pH and mineral N availability appeared to have a cooperative effect on bacterial abundance and community structure, with soil pH being the key influencing factor by canonical correspondence analysis. These results indicate that inorganic N rather than P fertilization affected both bacterial abundance and community composition in subtropical forests.

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Considering the problem of discrete texture synthesis and the time for texturing, this paper proposes a novel framework for synthesizing texture images based on discrete example-based elements. We start with extracting texture feature distribution from exemplars and then produce discrete elements based on the cluster algorithm. After initializing a texture image, we propose a texture optimization algorithm based on heuristic searching to improve the quality of the texture image. Final, we use a texture transfer method based on Convolutional Neural Network (CNN) to stylize the optimized texture image. Our results show that the proposed texture synthesis method can significantly improve the quality of discrete texture synthesis and effectively shorten the time for texture generation. INDEX TERMS Texture synthesis, discrete elements, cluster algorithm, heuristic searching, CNN.