Recently, research has investigated the role of the ruminant native microbiome, and the role microbes play in methane (CH4) production and mitigation. However, the variation across microbiome studies makes implementing impactful strategies difficult. The first objective of this study is to identify, summarize, compile, and discuss the current literature on CH4 mitigation strategies and how they interact with the native ruminant microbiome. The second objective is to perform a meta-analysis on the identified16S rRNA sequencing data. A literature search using Web of Science, Scopus, AGRIS, and Google Scholar will be implemented. Eligible criteria will be defined using PICO (population, intervention, comparator, and outcomes) elements. Two independent reviewers will be utilized for both the literature search and data compilation. Risk of bias will be assessed using the Cochrane Risk Bias 2.0 tool. Publicly available 16S rRNA amplicon gene sequencing data will be downloaded from NCBI Sequence Read Archive, European Nucleotide Archive or similar database using appropriate extraction methods. Data processing will be performed using QIIME2 following a standardized protocol. Meta-analyses will be performed on both alpha and beta diversity as well as taxonomic analyses. Alpha diversity metrics will be tested using a Kruskal-Wallis test with a Benjamini-Hochberg multiple testing correction. Beta diversity will be statistically tested using PERMANOVA testing with multiple test corrections. Hedge's g standardized mean difference statistic will be used to calculate fixed and random effects model estimates using a 95% confidence interval. Heterogeneity between studies will be assessed using the I2 statistic. Potential publication bias will be further assessed using Begg's correlation test and Egger's regression test. The GRADE approach will be used to assess the certainty of evidence. The following protocol will be used to guide future research and meta-analyses for investigating CH4 mitigation strategies and ruminant microbial ecology. The future work could be used to enhance livestock management techniques for GHG control. This protocol is registered in Open Science Framework (https://osf.io/vt56c) and available in the Systematic Reviews for Animals and Food (https://www.syreaf.org/contact).
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