Species pool and local assembly processes drive ß diversity of ammonia-oxidizing and denitrifying microbial communities in rivers along a latitudinal gradient.

Both regional species pool and local community assembly mechanism drive the microbial diversity patterns across geographical gradients. However, little has been done to separate their effects on the ß diversity patterns of microbial communities involved in nitrogen (N) cycling in river ecosystems. Here, we use high-throughput sequencing of the archaeal amoA, bacterial amoA, nirK, and nirS genes, null model, and neutral community model to distinguish the relative importance of species pool and local assembly processes for ammonia-oxidizing and denitrifying communities in river wetlands along a latitudinal gradient in eastern China. Results indicated that the ß diversity of the nirS-type denitrifying community co-varied with γ diversity and environmental heterogeneity, implying that regional species pool and heterogeneous selection explained variation in ß diversity. However, the ß diversity of ammonia-oxidizing and nirK-type denitrifying communities did not correlate with γ diversity and environmental heterogeneity. The continuous hump distribution of ß deviation along the latitudinal gradient and the lower species dispersal rate indicated that the dispersal limitation shaped the variation in ß diversity of ammonia-oxidizing and nirK-type denitrifying communities. Additionally, biotic interactions drove ammonia-oxidizing and nirS-type denitrifying communities by influencing species co-occurrence patterns. Our study highlights the importance of regional species pool and local community assembly processes in shaping geographical patterns of N-cycling microorganisms and extends knowledge of their adaptability to a continuously changing environment on a large scale.

Plant diversity is more important than soil microbial diversity in explaining soil multifunctionality in Qinghai-Tibetan plateau wetlands.

The Qinghai-Tibetan Plateau harbors rich and diverse wetlands that provide multiple ecological functions simultaneously. Although the relationships between biodiversity and wetland functioning have been well studied in recent decades, the links between the multiple features of plant and microbial communities and soil multifunctionality (SMF) remain unknown in the high-altitude wetlands that are extremely sensitive to human disturbance. Here, using the single function, averaging, weighted, and multiple-threshold methods, we calculated the SMF of Qinghai-Tibetan wetlands based on 15 variables associated with soil nutrient status, nutrient cycle, and greenhouse gas emission. We then related SMF to multidimensional (species, phylogenetic, and functional) diversity of plants and soil microorganisms and microbial network modules. The results showed that plant diversity explained more variance in SMF than soil microbial diversity, and plant species richness and phylogenetic distance were positive predictors of SMF. Bacterial network modules were more positively related to SMF than fungal network modules, and the alpha diversity of bacterial network modules contributed more to SMF than the diversity of the whole bacterial community. Pediococcus, Hirsutella, and Rhodotorula were biomarkers for SMF and had significant relationships with nitrogen mineralization and greenhouse gas emissions. Together, these results highlight the importance of plant diversity and bacterial network modules in determining the SMF, which are crucial to predicting the response of ecosystem functioning to biodiversity loss under intensifying anthropogenic activities.

Learner satisfaction-based research on the application of artificial intelligence science popularization kits.

The application of artificial intelligence science popularization kits in maker courses has promoted the rapid development of maker education. However, there exist few theoretical and empirical studies on the application of artificial intelligence science popularization kits in maker education. The theory of learner satisfaction can be used to explain learner motivation and outcomes with regard to participation in maker education using the artificial intelligence suite. Therefore, taking advantage of the opportunity the Zhejiang Action Plan for Promoting the Development of New Generation Artificial Intelligence (2019-2022) has provided, this study first conducted semi-structured interviews based on the results of a literature review and a questionnaire survey and then performed Pearson correlation analysis and regression analysis using SPSS 24.0 to explore the influencing factors of students' satisfaction with the use of artificial intelligence science popularization kits in education. The following results were obtained. (1) The correlation between grades and learners' satisfaction is not significant. (2) The use of a high-quality artificial intelligence science suite in the classroom will positively impact learners' satisfaction. (3) The degree of interaction with the artificial intelligence suite is negatively correlated with learners' satisfaction. (4) Teaching adaptability is significantly positively correlated with learner satisfaction. (5) Learners' individual characteristics have no significant positive correlation with learner satisfaction. Therefore, this study recommends focusing on suite quality, improving human-computer interaction, adopting a student-centered strategy, and aiming at improving the suitability of the curriculum.