Functional Redundancy and Niche Specialization in Honeybee and Varroa Microbiomes.
INTERNATIONAL MICROBIOLOGY(2024)
Institute of Biology and Ecology
Abstract
The honeybee (Apis mellifera) is a key pollinator critical to global agriculture, facing threats from various stressors, including the ectoparasitic Varroa mite (Varroa destructor). Previous studies have identified shared bacteria between Varroa mites and honeybees, yet it remains unclear if these bacteria assemble similarly in both species. This study builds on existing knowledge by investigating co-occurrence patterns in the microbiomes of both Varroa mites and honeybees, shedding light on potential interactions. Leveraging 16S rRNA datasets, we conducted co-occurrence network analyses, explored Core Association Networks (CAN) and assess network robustness. Comparative network analyses revealed structural differences between honeybee and mite microbiomes, along with shared core features and microbial motifs. The mite network exhibited lower robustness, suggesting less resistance to taxa extension compared to honeybees. Furthermore, analyses of predicted functional profiling and taxa contribution revealed that common central pathways in the metabolic networks have different taxa contributing to Varroa mites and honeybee microbiomes. The results show that while both microbial systems exhibit functional redundancy, in which different taxa contribute to the functional stability and resilience of the ecosystem, there is evidence for niche specialization resulting in unique contributions to specific pathways in each part of this host-parasite system. The specificity of taxa contribution to key pathways offers targeted approaches to Varroa microbiome management and preserving honeybee microbiome. Our findings provide valuable insights into microbial interactions, aiding farmers and beekeepers in maintaining healthy and resilient bee colonies amid increasing Varroa mite infestations.
MoreTranslated text
Key words
Apis mellifera,Varroa destructor,Microbiomes,Community assembly,Networks
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
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
Summary is being generated by the instructions you defined