Huangshan Mountain Research Articles

Characteristics of Pinus hwangshanensis Rhizospheric Fungal Community along Huangshan Mountain's Elevation Gradients, China.

Elevation gradients strongly influence the diversity pattern of soil microorganisms. To date, many studies have elucidated the response of soil microbes to changes in elevation gradients. However, the effects of these gradients on the assembly mechanisms and network complexity of rhizospheric microbial communities remain underexplored. To bridge this knowledge gap, this study assessed the response of rhizospheric fungal communities of Pinus hwangshanensis along different elevation gradients in the Huangshan Mountain scenic area with regard to diversity, community composition, and assembly mechanisms using high-throughput amplicon sequencing. The results revealed significant differences in rhizospheric fungal community composition across three elevation gradients. The soil organic matter and pH were the most relevant factors influencing the changes in rhizospheric fungal community composition. The rhizospheric fungal diversity was significantly lower at both low and high elevations compared to the medium elevation. The rhizospheric fungal community assembly showed a more deterministic process at low and high elevations than at the medium elevation, indicating that stronger environmental filtering contributed to reduced fungal diversity at the extremes of the elevation gradient. In addition, rhizospheric pathogens, particularly Dermateaceae, acted as keystone taxa, diminishing the stability of co-occurrence networks at the medium elevation. This study contributes to a more comprehensive understanding of rhizospheric fungal community patterns and their ecological functions along elevation gradients in mountainous regions.

Leaf functional traits and ecological strategies of common plant species in evergreen broad-leaved forests on Huangshan Mountain

Leaf functional traits and ecological strategies of common plant species in evergreen broad-leaved forests on Huangshan Mountain

Morpho-phylogenetic characterizations reveal a new species of Coccomyces on Cunninghamia lanceolata from China

Specimen of Coccomyces collected on Cunninghamia lanceolata, from Huangshan Mountain in Anhui Province, China, was identified as a new species, named here as Coccomyces cunninghamiae. Although multi-gene (ITS, nrLSU, mtSSU) phylogenetic analysis showed that the new species was closely related to C. anhuiensis, it differs significantly from C. anhuiensis in morphology by intraepidermal or subepidermal ascomata, well-developed paraphyses forming epithecium and shape of ascospores. Moreover, the ITS sequence similarity between them is only 87.86%. Specimens studied are deposited in the Reference Collection of Forest Fungi of Anhui Agricultural University, China (AAUF).

Climate remained a major driving factor of vegetation dynamics over the past 600 years in Huangshan Mountain, Southeast China

Holocene vegetation change is a complex process driven by climate and human activity, distinguishing their relative contributions is important to understand and adapt to the changing planet. Southeast China has a long history of human occupation and the evergreen broadleaved forests of the region have been strongly affected by human activity and replaced widely by anthropogenic vegetation in the last millennium. Pines exist both in natural and anthropogenic vegetation, therefore the interrelationship between pine and other species offers a means of differentiating natural from anthropogenic vegetation changes. Both climate and population in southeast China have undergone substantial change during the late Holocene, but the response of vegetation at centennial scale has not been fully resolved. In this study, we present a palynological analysis of a sediment core obtained from a high-elevation wetland on the Huangshan Mountain, which is known for its remarkably steep topography. The core provides evidence of vegetation history since the onset of the Little Ice Age. Results show that Huangshan vegetation shifted from montane mixed forest to upper montane grassland at the beginning of the Little Ice Age and that, although forest recovery thereafter was gradual, the coexistence of forest and grassland has been maintained since 1800 CE. Climate is shown to be the main driver of Huangshan vegetation change over the last six centuries as a whole, although the expansion of pines into broadleaved forests under the pressure of population growth, commonly reported at other sites in southeast China, is also observed in the Huangshan record from 1800 CE onward. Nevertheless, vegetation on the mountain appears to have been less impacted by human activity than in other parts of the region, possibly as result of the unique topography of Huangshan that restricts its accessibility. In conclusion, the study reveals climate still played an important role in driving last-millennium vegetation change.

Research on Spatio-Temporal Characteristics of Tourists' Landscape Perception and Emotional Experience by Using Photo Data Mining.

Mountainous scenic spots are important tourism resources, and the study of tourists' landscape perception and emotional preference when visiting them is beneficial to the management of scenic spots in order to improve the service quality and promote the protection, development, and utilization of scenic resources. In this paper, we use the location photo data of tourists at Huangshan Mountain to extract the visual semantic information of location photos, calculate the photo sentiment value, and mine the landscape perception and sentiment preference features of tourists using DeepSentiBank image recognition model and photo visual semantic quantification method. The results show the following: (1) Huangshan tourists mainly focus on nine types of photos, with the most attention paid to the category of mountain rock landscapes and the least attention paid to the category of animal landscapes. (2) In terms of spatial distribution, the landscape types of tourist photos show the spatial characteristics of "concentrated into a belt", "significant nucleus", and "fragmented distribution". The spatial variation of the emotional value of tourists' photos is significant, and the high values are mainly distributed at the entrances and exits, interchanges, and famous attractions. (3) On a temporal scale, the type of perception of the Huangshan location photograph landscape shows a significant imbalance. The emotional values of tourists' photos vary significantly, with a "slowly sloping straight line" type of emotional change on the seasonal scale, a "W" type of emotional change on the monthly scale, an "N" type of emotional change on the weekly scale, and an "M" type of emotional change on the hourly scale. This study attempts to explore the landscape perceptions and emotional preferences of tourists in mountainous scenic areas with new data and methods, aiming to promote the sustainable and high-quality development of mountainous scenic areas.

A New Discovery of Xu Xiake’s Whereabouts in Zhejiang Province from the Perspective of Digital Xu Studies

By extracting the tracing information of Xu Xiake’s travels to Zhejiang Province recorded in his The Travel Notes of Xu Xiake, this research constructed his tracing database by using geographic information system technology, and carried out detailed spatial analysis and visual expression of Xu Xiake’s travels. This research resolved two controversies. First, when Xu Xiake visited Zhejiang Province for the second time in 1616, he went into Kaihua (Anhui Province) from Changshan. After visiting Huangshan Mountain, he traveled across Tunxi to Changshan, then went through Jiangxi into Fujian Province. Second, on his way home after visiting Tiantai Mountain and Yandang Mountain for the third time, Xu Xiake and Xu Zhongzhao parted at Shangyu and went to Siming Mountain alone.

The complete chloroplast genome sequence of Salvia chienii E.Peter, 1936 (Lamiaceae)

Salvia chienii E.Peter is a medicinal herb mainly distributed in Huangshan Mountain of Anhui province, China. In this study, the first complete chloroplast genome of S. chienii was sequenced and assembled. The genome length was 151,530 bp and encoded 143 genes (91 protein-coding genes, eight rRNA genes, and 37 tRNA genes). The phylogenomic analysis showed that S. chienii was closely related to S. miltiorrhiza. Further evolutionary studies of the genus Salvia could benefit from the complete chloroplast genome of S. chienii present in this study.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Ecological Risk Assessment of World Heritage Sites Using RS and GIS: A Case Study of Huangshan Mountain, China

Ecological Risk Assessment of World Heritage Sites Using RS and GIS: A Case Study of Huangshan Mountain, China

Soil depth exerts stronger impact on bacterial community than elevation in subtropical forests of Huangshan Mountain

The elevational distribution of bacterial communities in the surface soil of natural mountain forests has been widely studied. However, it remains unknown if microbial communities in surface and sub-surface soils exhibit a similar distribution pattern with elevation. To do so, Illumina HiSeq sequencing was applied to study the alterations in soil bacterial communities of different soil layers, along an altitudinal gradient from 500 to 1100 m on Huangshan Mountain in Anhui Province, China. Our results revealed a significant higher diversity of the bacterial communities in surface soil layers than in subsurface layers. Adonis analysis showed that soil layer had a greater influence on the composition of the bacterial communities than the elevation. The distance-based multivariate linear model suggested that soil labile organic carbon and elevation were the main element influencing the bacterial community composition in surface and subsurface soils, respectively. A remarkable difference appeared between the co-occurrence network structures of bacterial communities in different soil layers. Compared with the subsurface soil, surface soil had more edges, average degree, and much higher clustering coefficient. The two-way ANOVA results highlighted the significant impact of soil layers on the topological properties of the network compared with that of elevation. The keystone species belonged to Rhodospirillaceae in the surface soil, while the OTUs belonged to Actinomycetales in the subsurface soil. Collectively, our results demonstrate that the effects of soil depth on soil bacterial community composition and network properties of subtropical forest in Huangshan Mountain were significantly higher than those of elevation, with different keystone species in different soil layers. These findings can be served as an important basis for better understanding the microbial functions influencing the maintenance of habitat heterogeneity, biodiversity, and ecosystem services in forests ecosystems.

Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing

To identify hygrothermal transfer patterns of exterior walls is a crucial issue in the design, assessment, and construction of buildings. Temperature and relative humidity, as sensor monitoring data, were collected from the outside of the wall to interior bamboo and wood composite sheathing over the year in Huangshan Mountain District, Anhui Province, China. Combining the machine learning method of reservoir computing (RC) with agglomerative hierarchical clustering (AHC), a novel clustering framework was built for better extraction of the characteristics of hygrothermal transfer on the time series data. The experimental results confirmed the hypothesis that the change in the temperature and relative humidity of the outside of the wall (RHT12) dominated the change of the interior sheathing (RHT11). The delay time between two adjacent peaks in temperature was 1 to 2 h, while that in relative humidity was 1 to 4 h from the outside of the wall to interior bamboo and wood composite sheathing. There was no significant difference in temperature peak delay time between April and July. Temperature peak delay time was 50 to 120 min. However, relative humidity peak delay time was 100 to 240 min in April, whereas it was 20 to 120 min in July. The impact formed a relatively linear relationship between outdoor temperature and relative humidity peak delay time. The hygrothermal transfer patterns were characterized effectively by the peak delays. The discovery of the hygrothermal transfer patterns for the bamboo and wood composite walls using the machine learning method will facilitate the development of energy-efficient and durable bamboo and wood composite wall materials and structures.

How to Improve Farmers’ Green Production Level in a Targeted Manner?

Improving farmers’ green production behavior can guarantee food safety at the source. In recent years, a rising number of studies have focused on food safety management and have provided general regulation recommendations. Unlike many studies, this study aimed to find targeted policy recommendations according to different spatial aggregations of non-green production behavior. In the current study, more than 800 tea farmers located in the Qinba and Huangshan Mountain regions of China were investigated. An order logit model was employed to evaluate the impact of government regulation and community governance on ignorant or unkind non-green tea production behavior. Furthermore, a multi-valued treatment effect model was also recruited to demonstrate the average treatment effect of government regulation and community governance. The results show that the difference in farmers’ ignorant or unkind non-green production behavior between regions is substantial but is similar in the same region. Farmers’ non-green production behavior is negatively impacted by community governance but not significantly affected by government regulation. Government regulation can effectively inhibit the occurrence of farmers’ unkind behavior, while community governance can effectively cause farmers’ unkind behavior. Thus, some measures to reduce local farmers’ non-green production behavior should be put forward according to the differences in the spatial distribution of non-green production behavior and the differences in the effect of government regulation and community governance.

Soil Depth Exerts Stronger Impact on Bacterial Community than Elevation in Subtropical Forests of Huangshan Mountain

Soil Depth Exerts Stronger Impact on Bacterial Community than Elevation in Subtropical Forests of Huangshan Mountain

Forecasting Daily Tourism Demand for Tourist Attractions with Big Data: An Ensemble Deep Learning Method

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning.

Spatiotemporal Characteristics of Drought in the North China Plain over the Past 58 Years

Understanding the spatiotemporal characteristics of regional drought is of great significance in decision-making processes such as water resources and agricultural systems management. The North China Plain is an important grain production base in China and the most drought-prone region in the country. In this study, the monthly standardized precipitation evapotranspiration index (SPEI) was used to monitor the spatiotemporal variation of agricultural drought in the North China Plain from 1960 to 2017. Seven spatial patterns of drought variability were identified in the North China Plain, such as Huang-Huai Plain, Lower Yangtze River Plain, Haihe Plain, Shandong Hills, Qinling Mountains Margin area, Huangshan Mountain surroundings, and Yanshan Mountain margin area. The spatial models showed different trends in different time stages, indicating that the drought conditions in the North China Plain were complex and changeable in the past 58 years. As an important agricultural area, the North China Plain needs more attention since this region shows a remarkable trend of drought and, as such, will definitely increase the water demand for agricultural irrigation. The strong correlation between these spatial distribution patterns indicates that the climate and weather conditions leading to drought are consistent and that drought conditions are independent for regions that are not correlated. If this trend continues, the characteristics of drought variability in the North China Plain will become more complex, and a more detailed water management strategy will be needed to address the effects of drought on agro-ecosystems. Recognizing the drought variability in the North China Plain can provide a basis for agricultural disaster reduction planning and water resources allocation.

Tibetan Macaques with Higher Social Centrality and More Relatives Emit More Frequent Visual Communication in Collective Decision-Making.

Simple SummaryIt is well known that visual communication plays an important role in collective decision-making. However, there is not much research on the influencing factors of visual signals, especially kinship and social relations. In this study, we not only confirmed the function of visual communication in collective decision-making, but also found the effect of kinship and social relations on visual communication. Tibetan macaques with higher social centrality and more relatives emit more frequent visual communication, providing a reference for further research on decision-making. Understanding the link between communication and decision-making can elucidate the powers of group maintenance in animal societies.Animals on the move often communicate with each other through some specific postures. Previous studies have shown that social interaction plays a role in communication process. However, it is not clear whether the affinity of group members can affect visual communication. We studied a group of free-ranging Tibetan macaques (Macaca thibetana) at Huangshan Mountain, China, and answered whether and how social centrality or relatives matter in visual signals during group movement using Tobit regression modeling. All individuals emitted the signals of back-glances and pauses in collective movement. The emission of two signals decreased with the number of participants increased. The back-glance and pause signals emitted by the participating individuals were stronger as the position moved backward in the group. Sex, age, and rank had no significant influence on back-glance and pause signals. Individuals with higher social centrality would emit more pause signals, but social centrality had no effect on the back-glance signal. Individuals with more relatives in the group had more back-glance signals, but this had no effect on the pause signal. This study verifies that social centrality and the number of relatives have effects on visual signals in Tibetan macaques. We provide insights into the relationship between communication behaviors and group cooperation in social animals.

Characteristics and Perception of Cultural Tourism Resources in Mt. Huangshan Scenic Spot

Taking Huangshan Mountain Scenic Spot as the research object, this paper explores the types, characteristics and perception of the cultural tourism resources of the double heritage scenic spot through collecting previous literature, on-site investigation and comparative analysis and other research methods. The results show that: (1) There are four types of cultural resources in Mt. Huangshan Scenic Spot, namely, cliff stone carving, historical structure, literary works and cultural spirit; (2) Compared with Tai Mountain, Emei Mountain, Wuyi Mountain and other world cultural and natural heritage sites tourists have low perception of cultural tourism resources in Huangshan Mountain Scenic Spot; (3) Factors such as typical natural attribute, single tourism product and seasonal tourists flow are the main influencing factors of low perception of cultural tourism resources in Huangshan Mountain Scenic Spot. Paying attention to resource mining, strengthening product design and expanding the scope of publicity, etc. can increase tourists’ perception of cultural tourism resources in Huangshan Mountain Scenic Spot.

Development and validation of a tourism fatigue scale

Abstract Fatigue is a pervasive physiological and psychological phenomenon in tourism experiences, but remains almost unexplored in the existing literature on tourist perception and behavior. The possible reason for this deficiency is the vagueness of the tourism fatigue concept and the lack of a measurement tool in addition to the influence of the traditional paradigms and ideas of the tourism research. Therefore, the purpose of this study was to develop a scale for measuring tourism fatigue based on its conceptualization. Taking Zhouzhuang Ancient Town and Huangshan Mountain as examples, 926 samples were collected through two rounds of data collection. Following construct definition, initial item production, scale purification, EFA, CFA, reliability and validity testing, a four-dimensional (physical, motivational, affective and cognitive) tourism fatigue model with 16 items was established. This study extends the current knowledge of tourist perceptions and behaviors and provides a tool for further related investigation for academics and managers.

Cultural worldview and cultural experience in natural tourism sites

Cultural worldview (CW) refers to people's underlying general attitudes such as basic beliefs and perceptions of a culture. The cultural experience (CE) in tourism is multi-dimensional, but little research has paid attention to the relationship between CW and CE in cultural tourism, especially with regard to nature sites. This paper explores the scales of CW and CE in the Chinese context in order to examine their relationship and compare the difference between high and low self-cultivation groups. Taking Huangshan Mountain as the study area, 253 usable questionnaire forms from 2016 to 322 usable questionnaire forms from 2017 were collected. Through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM) and multi-group analysis, the following results were obtained: 1) the scales of CW and CE in the Chinese context were identified; 2) the cultural linkages, cultural protection and Chinese traditional culture of nature in CW positively affected most dimensions of CE (conservation education, scientific knowledge, person-valuing and high culture); and 3) for the high self-cultivation group, the relationships between CW and CE were stronger and most of the latent mean scores were greater than those of the low self-cultivation group. This study contributes to the research in both cultural and nature tourism and social psychology, and has practical implications for destinations.

Daily tourism volume forecasting for tourist attractions

A novel approach based on long short-term memory (LSTM) networks that can incorporate multivariate time series data, including historical tourism volume data, search engine data and weather data, is proposed for forecasting the daily tourism volume of tourist attractions. The proposed approach is applied to forecast the daily tourism volume of Jiuzhaigou and Huangshan Mountain Area, two famous tourist attractions in China. Through these two applications, the validity of the proposed approach is verified. In addition, the forecasting power of the approach with historical data, search engine data and weather data is stronger than that without search engine data or without both search engine data and weather data, which provides evidence that search engine data and weather data are of great significance to tourism volume forecasting.