Search Results (7)

Alpine ecosystems, as one of the most representative terrestrial ecosystems, have garnered significant attention due to their susceptibility to human activities and climate change. However, the distribution patterns and driving factors of alpine soil bacterial communities remain to be further explored, especially for different dominant phyla. This study investigated the soil bacterial community composition, elevational patterns, and relationships between bacterial diversity and environmental factors at four elevation gradients (2406–3204 m) on Mt. Taibai, Qinling Mountains, China, using 16S rRNA sequencing. The results showed that the dominant bacterial phyla were Acidobacteria, Actinobacteria, Proteobacteria, and Chloroflexi, accounting for over 69% of the bacterial sequences in soil samples. Dominant bacterial communities exhibit distinct elevation gradient patterns in diversity and community structure. The α-diversity of Actinobacteria and Chloroflexi decreases with increasing elevation, whereas that of Proteobacteria and Acidobacteria increases. Moreover, the community structure of Actinobacteria shows greater variation across elevations than the other three dominant bacterial groups, with significant differences observed among elevations. Redundancy analysis and distance decay analysis revealed that elevation was significantly correlated with the soil bacterial community structure (p < 0.01). Different dominant bacterial communities were regulated by distinct environmental factors, providing strong evidence for understanding microbial community assembly. Therefore, the α- and β-diversity of soil bacteria on Mt. Taibai exhibit distinct elevational variations, and elevation-driven plant diversity and pH may be key factors shaping the spatial distribution of soil bacteria. Full article

Analyzing the spatiotemporal patterns of atmospheric temperature in sensitive areas is critically important for understanding the broader implications of global climate change, which remains a prominent topic in geosciences. It also plays a crucial role in advancing sustainable development. This study utilized daily minimum, maximum, and mean temperature data from twelve meteorological stations across the South and North Mts. Qinling (Qinling Mountains). Employing trend analysis, the Mann–Kendall mutation test, and Morlet wavelet analysis, we explored the predominant temperature trends and characteristics from 1971 to 2022. Our findings revealed consistent inter-annual warming trends in both regions, with more rapid temperature increases in the North compared to the South. Notably, significant shifts occurred in 2003 for both mean and minimum temperatures in the North, while the maximum and minimum temperature values were recorded in the 2010s and 1980s, respectively. Both regions exhibited a primary temperature fluctuation cycle of 28 years. Seasonally, the strongest warming effects appeared in spring, with the weakest in autumn, and moderate effects in winter and summer, indicating that spring contributes most significantly to regional warming. Monthly analysis showed positive temperature trends across all months, with higher rates in the North. The weakening temperature boundary effect of the Mts. Qinling suggested a weakening North–South division, particularly highlighted by the northward shift of the 1 °C isotherm curve for the coldest month, moving away from the previously observed 0 °C isotherm. This northward shift highlights the differential warming rates between the northern and southern regions. Overall, the analysis confirms a robust warming trend, with notable fluctuations in January’s temperatures since 1998, suggesting the Mts. Qinling’s emerging role as a climatic divider in the Chinese Mainland. This introduces new challenges for regional ecosystems, agricultural production, and water resource management, highlighting the pressing need to advance regional sustainable development in the face of climate change. Full article

With the development of satellite remote sensing technology, a substantial quantity of remote sensing data can be obtained every day, but the ability to extract information from these data remains poor, especially regarding intelligent extraction models for vegetation information in mountainous areas. Because the features of remote sensing images (such as spectral, textural and geometric features) change with changes in illumination, viewing angle, scale and spectrum, it is difficult for a remote sensing intelligent interpretation model with a single data source as input to meet the requirements of engineering or large-scale vegetation information extraction and updating. The effective use multi-source, multi-resolution and multi-type data for remote sensing classification is still a challenge. The objective of this study is to develop a highly intelligent and generalizable classification model of mountain vegetation utilizing multi-source remote sensing data to achieve accurate vegetation extraction. Therefore, a multi-channel semantic segmentation model based on deep learning, FCN-ResNet, is proposed to integrate the features and textures of multi-source, multi-resolution and multi-temporal remote sensing data, thereby enhancing the differentiation of different mountain vegetation types by capturing their characteristics and dynamic changes. In addition, several sets of ablation experiments are designed to investigate the effectiveness of the model. The method is validated on Mt. Taibai (part of the Qinling-Daba Mountains), and the pixel accuracy (PA) of vegetation classification reaches 85.8%. The results show that the proposed multi-channel semantic segmentation model can effectively discriminate different vegetation types and has good intelligence and generalization ability in different mountainous areas with similar vegetation distributions. The multi-channel semantic segmentation model can be used for the rapid updating of vegetation type maps in mountainous areas. Full article

The Qinling Mountains are located on the dividing line between the north and the south of China. Mt. Taibai, the study site, is the highest peak in the Qinling Mountains and also the highest peak in eastern mainland China. At Mt. Taibai, several dendroecological studies have been conducted on the relationship between tree-ring indices and climatic factors using traditional statistical methods. In this study, the response of Larix chinensis Beissn radial growth to climatic factors was explored in the treeline area of Mt. Taibai using the process-based Vaganov–Shashkin-Lite (VSL) model for the first time. The conclusions were obtained according to the analysis of the L. chinensis tree-ring samples collected from both the northern and southern slopes of Mt. Taibai. The VSL model showed that temperature limits L. chinensis growth during the entire growing season, while the model did not indicate precipitation as a limiting factor. The model showed significant positive correlations between the simulated and observed tree-ring chronologies for 1959–2013, excluding the uppermost sample site on the northern slope. However, the model performance deteriorated with increasing altitude, which may be due to the decreased sensitivity of radial growth to climatic factors above the treeline. Full article

Larix chinensis Beissn., as a native, dominant and climate-sensitive coniferous species at Mount Taibai timberline, Qinling mountains, is rarely disturbed by anthropogenic activities; thus, it is an ideal proxy for the investigation of climate change or vegetation evolution. In this study, we applied dendrochronological methods to the L. chinensis tree-ring series from Mt. Taibai and investigated the relationships between tree-ring widths and NDVI/climate factors using Pearson correlation analysis. On the basis of the remarkable positive correlations (r = 0.726, p < 0.01, n = 23) between local July normalized difference vegetation indices (NDVI) and tree-ring width indices, the regional 146-year annual maximum vegetation density was reconstructed using a regression model. The reconstructed NDVI series tracked the observed data well, as the trans-function accounted for 52.8% of observed NDVI variance during AD 1991–2013. After applying an 11-year moving average, five dense vegetation coverage periods and six sparse vegetation coverage periods were clearly presented. At a decadal scale, this reconstruction was reasonably and negatively correlated with a nearby historical-record-based dryness/wetness index (DWI), precisely verifying that local vegetation cover was principally controlled by hydrothermal variations. Spectral analysis unveiled the existence of 2–3-year, 2–4-year, 5–7-year and 7–11-year cycles, which may potentially reflect the connection between local NDVI evolution and larger-scale circulations, such as the El Niño–Southern Oscillation (ENSO) and solar activity. This study is of great significance for providing a long-term perspective on the dynamics of vegetation cover in the Qinling mountains, and could help to guide expectations of future forest variations. Full article

A new species of Pseudopyrochroa Pic, 1906, P. reni Pan & Young, n. sp., is described from the western region of Mt. Qinling, China. Larvae, pupae, and adults were associated using molecular phylogenetic analyses based on mtDNA COI barcode sequences. All three stages are described and illustrated. Additionally, preliminary phylogenetic relationships among five genera and 14 species of Pyrochroidae, including Pseudopyrochroa, are hypothesized based on COI sequence data. The fauna of Pyrochroidae from the Mt. Qinling biodiversity conservation area is discussed. Full article

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters. Full article