Effect of fire and post-fire management on soil microbial communities in a lower subtropical forest ecosystem after a mountain fire.

Wildfires and post-fire management exert profound effects on soil properties and microbial communities in forest ecosystems. Understanding microbial community recovery from fire and what the best post-fire management should be is very important but needs to be sufficiently studied. In light of these gaps in our understanding, this study aimed to assess the short-term effects of wildfire and post-fire management on both bacteria and fungi community composition, diversity, structure, and co-occurrence networks, and to identify the principal determinants of soil processes influencing the restoration of these communities. Specifically, we investigated soil bacterial and fungal community composition, diversity, structure, and co-occurrence networks in lower subtropical forests during a short-term (<3 years) post-fire recovery period at four main sites in Guangdong Province, southern China. Our results revealed significant effects of wildfires on fungal community composition, diversity, and co-occurrence patterns. Network analysis indicated reduced bacterial network complexity and connectivity post-fire, while the same features were enhanced in fungal networks. However, post-fire management effects on microbial communities were negligible. Bacterial diversity correlated positively with soil microbial biomass nitrogen, soil organic carbon, and soil total nitrogen. Conversely, based on the best random forest model, fungal community dynamics were negatively linked to nitrate-nitrogen and soil water content. Spearman's correlation analysis suggested positive associations between bacterial networks and soil factors, whereas fungal networks exhibited predominantly negative associations. This study elucidates the pivotal role of post-fire management in shaping ecological outcomes. Additionally, it accentuates the discernible distinctions between bacterial and fungal responses to fire throughout a short-term recovery period. These findings contribute novel insights that bear significance in evaluating the efficacy of environmental management strategies.

Effects of different takeover request interfaces on takeover behavior and performance during conditionally automated driving.

Automated driving is a developing trend that is coming to the consumer market, and conditionally automated driving (CAD) is anticipated to become the primary automated driving system. For enhancing both the comfort and security of human drivers in self-driving cars, the most significant concern of CAD is ensuring that not only can the driver conduct non-driving related tasks (NDRT) while automated driving is in progress, but also quickly and competently take over when the system reaches a limit and issues a takeover request (TOR). However, the level of distraction by NDRTs may affect the transition from automated driving to the human driver taking over. The focus of the present study was allowing a driver immersed in NDRTs to discover the TOR and take control of the driving quickly. A 3×2×2 factor experimental design was used: vehicle display interface information load (basic vs. prediction vs. advanced prediction interfaces); TOR information load (directional vs. non-directional information notifications); and degree of NDRT immersion (not performing vs. performing an NDRT when TOR prompt was issued). 48 participants were recruited, and different automotive display interfaces were used as TOR prompts with different information loads during driving to analyze the takeover behavior, performance, and subjective perception of the drivers, who were immersed in a smartphone-related task. The takeover process out of NDRT immersion was found to be more efficient with the advanced prediction interface, compared to the other two interfaces. All groups achieved faster takeovers and demonstrated better takeover performance if given directional rather than non-directional information, regardless of interface type or NDRT immersion.