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. 2025 Mar 27;13(4):758.
doi: 10.3390/microorganisms13040758.

Characterization and Assembly Dynamics of the Microbiome Associated with Swine Anaerobic Lagoon Manure Treated with Biochar

A Nathan Frazier  1 , William Willis  1 , Heather Robbe  1 , Anna Ortiz  1 , Jacek A Koziel  1   2
Affiliations

Characterization and Assembly Dynamics of the Microbiome Associated with Swine Anaerobic Lagoon Manure Treated with Biochar

A Nathan Frazier et al. Microorganisms. .

Abstract

Biochar has significant potential for livestock microbiomes and crop agriculture regarding greenhouse gas emissions reduction. Therefore, a pilot study was designed to investigate the effect of biochar application on the surface of swine manure from an open lagoon and the associated microbial communities. Samples were collected from four different treatment groups: control (n = 4), coarse biochar (n = 4), fine biochar (n = 4), and ultra-fine biochar (n = 4). Additionally, aged manure in bulk was collected (n = 4) to assess alterations from the control group. The method of 16S rRNA amplicon sequencing along with microbial analyses was performed. Diversity was significantly different between aged manure in bulk samples and all treatment groups (Kruskal-Wallis; p < 0.05). Additionally, distinct community compositions were seen using both weighted and unweighted UniFrac distance matrices (PERMANOVA; p < 0.01). Differential abundance analysis revealed four distinct features within all treatment groups that were enriched (q < 0.001): Idiomarina spp., Geovibrio thiophilus, Parapusillimonas granuli, and an uncultured Gammaproteobacteria species. Similarly, Comamonas spp. and Brumimicrobium aurantiacum (q-value < 0.001) were significantly depleted by all the treatments. Stochastic and functional analyses revealed that biochar treatments were not deterministically altering assembly patterns, and functional redundancy was evident regardless of compositional shifts.

Keywords: ammonia; biochar; greenhouse gas emissions; microbial ecology; swine manure.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Microbial community composition PCoA plots. (A) Weighted UniFrac distance was used to analyze community composition. (B) Unweighted UniFrac distance was used to measure differences in community membership.
Figure 2
Figure 2
Diversity and taxonomic profiles. (A) Alpha diversity differences between treatment types were measured using observed richness, Shannon’s index, and Faith’s PD. (B) Taxonomic profiles for each treatment type were analyzed at the phylum level.
Figure 3
Figure 3
Differential abundance testing using ANCOM-BC. The top five enriched and depleted microbial taxa were recorded for CON (A), COR-biochar (B), FIN-biochar (C), and UF-biochar (D).
Figure 4
Figure 4
The top five microbial drivers by assignment strength at the family level for each treatment type.
Figure 5
Figure 5
Dual dendrogram of the top 30 KEGG orthologs associated with each sample following Z-score normalization.
See this image and copyright information in PMC

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