Today East Asia harbors many “relict” plant species whose ranges were much larger during the Paleogene-Neogene and earlier. The ecological and climatic conditions suitable for these relict species have not been identified. Here, we map the abundance and distribution patterns of relict species, showing high abundance in the humid subtropical/warm-temperate forest regions. We further use Ecological Niche Modeling to show that these patterns align with maps of climate refugia, and we predict species’ chances of persistence given the future climatic changes expected for East Asia. By 2070, potentially suitable areas with high richness of relict species will decrease, although the areas as a whole will probably expand. We identify areas in southwestern China and northern Vietnam as long-term climatically stable refugia likely to preserve ancient lineages, highlighting areas that could be prioritized for conservation of such species.

The outbreak of COVID-19 is a public health crisis that has had a profound impact on society. Stigma is a common phenomenon in the prevalence and spread of infectious diseases. In the crisis caused by the pandemic, widespread public stigma has influenced social groups. This study explores the negative emotions arousal effect from online public stigmatization during the COVID-19 pandemic and the impact on social cooperation. We constructed a model based on the literature and tested it on a sample of 313 participants from the group being stigmatized. The results demonstrate: (1) relevance and stigma perception promote negative emotions, including anxiety, anger, and grief; (2) the arousal of anger and grief leads to a rise in the altruistic tendency within the stigmatized group; and (3) stigmatization-induced negative emotions have a complete mediating effect between perceived relevance and altruistic tendency, as well as perceived stigma and altruistic tendency. For a country and nation, external stigma will promote the group becoming more united and mutual help. One wish to pass the buck but end up helping others unintentionally. We should not simply blame others, including countries, regions, and groups under the outbreak of COVID-19, and everyone should be cautious with the words and actions in the Internet public sphere.

No abstract

Information-sharing behavior is affected by identity recognition perception. The current study aims to delve into the impact of familiarity and anonymity on information-sharing behavior, and the mediating role of intrinsic motivations on WeChat Moments. We hypothesized a mediator role of intrinsic motivations in the relationship between an individual’s perceptions and information sharing. Based on the self-determination theory, a model was created and tested using a sample of 531 frequent users. In this study, these users were asked to use WeChat Moments, the most popular mobile private social networking site in China. The results demonstrate the significance of familiarity and identifiability in an interpersonal relationship, when using social networking sites. Moreover, the influence of perceived anonymity on information-sharing behavior, which is entirely mediated by intrinsic motivation has been validated from an empirical perspective. Our findings extend previous studies by showing the totally mediated effect of perceived anonymity on information-sharing behavior on WeChat Moments and the influential mechanism of intrinsic motivation. The results will inform researchers about the importance of incorporating the interpersonal structural features and intrinsic motivation of social networking sites into future studies on online information-sharing behavior. Important ways to promote attention and share information involve building a familiar relationship with communities and equipping oneself with off-line relations. Final indications for future developments are provided, with a special emphasis on the development of these findings in various social networking sites contexts.

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.