Multiple Anthropogenic Environmental Stressors Structure Soil Bacterial Diversity and Community Network
Soil Biology and Biochemistry(2024)SCI 1区
Abstract
Microbial communities in many ecosystems are suffering a wide range of environmental stressors induced by anthropogenic perturbations. While the impacts of a single stressor have been extensively estimated in numerous studies, the responses of microbial communities to multiple environmental stressors simultaneously are still poorly understood. In the current study, we investigated the taxonomic diversity, community resistance, and co- occurrence interaction of soil bacterial communities treated with different numbers of environmental stressors by conducting 136 microcosms. We found that the richness and Shannon diversity of the soil community decreased significantly from 1430 to 6.54 in the mono-factor treatments to 920 and 5.77 in the hepta-factor treatments. The counts of nodes and edges of the soil microbial networks decreased with the increasing stressor number, potentially indicating that multiple stressors can reduce the network size. Multiple stressors increased the community resistance potential to environmental disturbance. Additionally, the network cohesion suggested that the cooperative and competitive behaviors between microorganisms were induced by multiple stressors. The observation could be potentially due to the enrichment of the generalists by multiple environmental stressors. Although only a handful of stressors were included, our study still indicated that multiple environmental stressors would lead to diversity loss via deterministic processes.
MoreTranslated text
Key words
Multiple environmental stressors,Bacterial communities,Diversity,Dissimilarity,Network interaction
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper