The investigation of taxonomic diversity within island plant communities stands as a central focus in the field of island biogeography. Phylogenetic diversity is crucial for unraveling the evolutionary history, ecological functions, and species combinations within island plant communities. Island effects (area and isolation effect) may shape species distribution patterns, habitat heterogeneity affects habitat diversity, and anthropogenic disturbances can lead to species extinction and habitat destruction, thus impacting both species diversity and phylogenetic diversity. To investigate how taxonomic and phylogenetic diversity in island natural plant communities respond to island effects, habitat heterogeneity, and anthropogenic disturbances, we took the main island of Haitan (a land-bridge island) and nine surrounding islands (oceanic islands) of varying sizes as the subjects of our study on the Pingtan islands. We aim to elucidate the influence of island effects, habitat heterogeneity, and anthropogenic disturbances on taxonomic and phylogenetic diversity. The results showed that, (1) Both the taxonomic and phylogenetic diversity of plants on the Pingtan islands followed the island area effect, indicating that as the island area increases, both taxonomic and phylogenetic diversity also increase. (2) Island effects and habitat heterogeneity were found to enhance taxonomic and phylogenetic diversity, whereas anthropogenic disturbances were associated with a decrease in both taxonomic and phylogenetic diversity. Furthermore, the synergistic influence of island effects, habitat heterogeneity, and anthropogenic disturbances collectively exerted a negative impact on both taxonomic and phylogenetic diversity. (3) The contribution of explanatory variables of anthropogenic disturbances for taxonomic and phylogenetic diversity was higher than that of island effects and habitat heterogeneity. Additionally, the contribution of the explanatory variables under the combined influence of island effects, habitat heterogeneity, and anthropogenic disturbances is higher than that of the individual variables for island effects and habitat heterogeneity. These findings suggest that anthropogenic disturbances emerged as the dominant factors influencing both taxonomic and phylogenetic diversity. These findings demonstrate the intricate interplay between island effects, habitat heterogeneity, and anthropogenic disturbances, highlighting their combined influence on both taxonomic and phylogenetic diversity on island.

Negative air ions (NAIs) in forest air contribute to air purification, improve respiratory system function, enhance immunity, reduce fatigue, and alleviate stress and anxiety. However, NAIs are easily influenced by meteorological factors, air quality factors, and radiation factors, and the mechanisms of these influences remain unclear. To investigate the dynamic changes of NAIs and their driving factors, this study focuses on the long-term monitoring of NAIs in the Yunzhong Mountain forest of the South Asian subtropical region in China. Using linear mixed models, structural equation models, and other ecological niche models, the study explores the characteristics of the dynamic changes in NAIs and their responses to meteorological factors, air quality factors, and radiation factors in the Yunzhong Mountain forest. The results showed that: (1) The seasonal variation of NAIs in the Yunzhong Mountain forest followed the pattern of summer > autumn > winter > spring. In terms of monthly dynamics, the highest concentrations of NAIs occurred in August. Regarding daily dynamics, the concentration of NAIs gradually increased from early morning, reaching its peak at 8:00 AM, after which it declined, reaching the lowest concentrations between 11:00 AM and 12:00 PM. (2) Correlation analysis showed that the concentrations of NAIs were significantly negatively correlated with NO₂, air temperature (AT), PM₁, PM_2.5, and PM₁₀, and significantly positively correlated with air humidity (AH). However, generalized linear mixed analysis indicated that PM_2.5, O₃, NO₂, and air pressure (AP) had significant negative effects on the seasonal, monthly, and daily concentrations of NAIs, while air humidity and temperature exhibited significant positive effects. Moreover, air quality factors had a greater impact than meteorological factors on seasonal and monthly concentrations, whereas meteorological factors had a more pronounced influence on daily negative air ion concentrations. (3) The latent variable of air quality factors had a direct negative effect on the concentration of NAIs, with O₃, PM₁, and PM_2.5 exerting significant indirect negative effects through the air quality factors. The latent variable of meteorological factors had a significant positive effect on the concentration of NAIs. Additionally, radiation factors did not have a direct significant effect on negative air ions, but radiation factors (ultraviolet radiation and net radiation) produced significant indirect positive effects on NAIs through meteorological factors and air quality. The concentration of NAIs in the Yunzhong Mountain forest is highest between 7:00 and 8:00 AM during July and August in the summer, making it the most suitable time for outdoor activities. Meteorological factors, air quality, and radiation factors all influence the NAIs in Yunzhong Mountain. Even in the absence of direct effects, these factors can have indirect impacts through other variables.

Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities.

In recent years, as civilization and human society have progressed, the potential and innovative capacity of various sectors of forest therapy have increasingly been recognized. However, the landscape of forest therapy is characterized by significant disparities in its distribution and uneven development patterns. Therefore, a comprehensive analysis of the factors influencing the distribution of forest therapy bases is crucial for optimizing the organization and allocation of resources within this industry, thereby promoting the growth of the forest therapy bases. This research delves into the spatial arrangement of forest therapy bases within Fujian Province, southern China. This study employs the nearest neighbor index, geographic concentration index, kernel density index, scale index, spatial autocorrelation analysis, and redundancy analysis to identify the primary factors influencing the geographical distribution of the bases. The study reveals three key findings about the spatial distribution of forest therapy bases in Fujian Province: (1) Centers are predominantly located in Nanping and Sanming, with a development pattern moving eastward and southward from Jianning and Taining in Sanming. (2) An imbalance is evident in the distribution, where areas with higher center concentrations exhibit a stronger spatial autocorrelation, characterized by high-density clusters. (3) Economic and environmental variables substantially affect center placement. At the municipal level, GDP, number of tourists, and forest coverage are significant. Conversely, at the district or county level, determinants include forest coverage, number of primary and secondary school students, forest land area, and GDP. Thus, it is suggested that the selection of bases for future forest therapy and the development of related industries should take into account local economic, environmental, and social factors. It aims to offer a scientific basis for planning forest therapy, potentially spreading its benefits to more areas.