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This paper examines the effects of automation on seven different professional sectors, and the degree of uncertainty this creates among current and soonto- be professionals It then looks at whether or not the individuals concerned make decisions accordingly, and if so, what the nature of these decisions are. A mixed method approach was chosen, incorporating a quantitative survey and qualitative interviews. The survey investigated 106 students' decisions regarding future career prospects and if they intend to develop skills relevant to automation. The interviews were conducted with 11 individuals working in fields affected by automation, or in which they have knowledge of the development of this technology. Comparing these two groups: workers and students entering the workforce within 0-5 years, demonstrated that students were more adaptive to automation (44% were learning to code or considering it vs 0% of professionals), despite a clear sample size limitation. Due to time constraints, a quantitative survey with professionals was not possible. All interviewees acknowledged that automation will replace their job or change it significantly. However, only the teacher was subsequently concerned about job security. The other interviewees' relaxed view may be explained by proximity to pension or strong unions. All interviewees acknowledged that automation will replace their job or change it significantly. However, only the teacher was subsequently concerned about job security. The other interviewees' relaxed view may be explained by proximity to pension or strong unions. [ABSTRACT FROM AUTHOR]

The diversity and interactions of soil fungal community are the key to maintain the diversity and stability of ecosystem. In this study, we examined the structure, diversity and co-occurrence networks of fungal community in rhizosphere and non-rhizosphere soils of planted and natural Picea asperata forests using high-throughput sequencing technique and bioinformatic methods. The results showed that Inocybaceae and Sebacinaceae were dominant family in soils of planted and natural forests, respectively. At the genus level, Inocybe was dominant one in soils of planted and natural forests. There were significant differences in β -diversity of fungal communities between rhizosphere and non-rhizosphere soils in both planted and natural forests. There were no significant correlations between environmental variables and the relative abundance and α -diversity of fungal communities. Herb layer coverage, soil water content, total organic carbon concentration, and plant species richness played important roles in explaining the variations of β -diversity of fungal communities. Results of the network analysis showed that the negative correlations were dominant among soil fungal communities in natural forest, suggesting that the competition of different groups in natural forest. Moreover, there were more negative correlations in non-rhizosphere soils than in rhizosphere soils, which indicated that fungal communities in non-rhizosphere soils comprised more competitive network structure than in the rhizosphere soils. Biomarker species were identified based on differential abundance analysis. Sebacinaceae was the single shared keystone species in the fungal network which had significant differences among rhizosphere and non-rhizosphere soils of planted and natural forests. Therefore, it is suggested that the variation of differential species in the soil fungal communities between the planted and natural forest might had limited influence on the stability of the community of planted and natural forests.

Methane (CH 4 ) is the second abundant greenhouse gas. Aerobic methane oxidations of topsoil in forest are identified as an important sink of atmosphere methane, playing critical roles in carbon cycle and global warming alleviation. Comparing the community structure and diversity of methane oxidizing bacteria (MOBs) in different vegetation ecosystems could provide new insights into the relationships between vegetation types and community of MOBs. In this study, we collected 92 soil samples from four types of vegetation along the elevation gradient of Gongga Mountain, and further assessed the community structure, diversity and potential drivers of MOBs across the four vegetation types using Miseq sequencing and bioinformatics methods. The result indicated that MOBs community compositions in evergreen and deciduous broad-leaved forest (EDBF) and coniferous and broad-leaved mixed forest (CBMF) was more similar, whereas in dark coniferous forest (DCF) and shrub meadow area (SMA) soils, MOB composition was more similar. In the four types of vegetation, α-diversities of MOBs in EDBF and CBMF were significantly higher than that in the other three vegetation types (P＜0.001), whereas β-diversities in soils of DCF and SMA were significantly higher than those in EDBF and CBMF (P＜0.001). Spearman analysis indicated that the relative abundance of different MOBs in four types of vegetation showed different responses to environmental change. Factors including soil total nitrogen, conductivity and soil temperature appeared to govern the variation of α-diversity across the four vegetation types. Partial Mantel test and redundancy ana-lysis (RDA) suggested that environmental parameters likely contributed more to diversity variations in soils from EDBF and CBMF, whereas diversity variations in soils from DCF and SAM might be driven by other potential environmental factors or mechanism. Precipitation appeared to drive the β-diversity variation between vegetation of EDBF and CBMF and vegetation of DCF and SAM. Our study suggested that the community structure and diversity variation of MOBs in different vegetation types might be governed by both soil properties and climate change.