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Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH/animal·d, ANIM-B models) and CH yield (g CH/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rum

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific. © 2015 Macmillan Publishers Limited.

Strains C23T and HBSQ001 were isolated from solar salterns and are novel square-shaped, aerobic, extremely halophilic members of the domain Archaea and family Halobacteriaceae. Cells stained Gram-negative and grew optimally in media containing 18 % salts at around neutral pH. Mg2+ is not required. The DNA G+C content of both isolates was 46.9 mol% and DNA–DNA cross-hybridization showed a relatedness of 80 %. Their 16S rRNA gene sequences showed only 2 nucleotide differences (99.9 % identity) and phylogenetic tree reconstructions with other recognized members of the Halobacteriaceae indicated that they formed a distinct clade, with the closest relative being Halogeometricum borinquense PR 3T (91.2 % sequence identity). The major polar glycolipid of both isolates was the sulfated diglycosyl diether lipid S-DGD-1. Electron cryomicrosopy of whole cells revealed similar internal structures, such as gas vesicles and polyhydroxyalkanoate granules, but the cell wall of isolate HBSQ001 displayed a more complex S-layer compared with that of isolate C23T. The phenotypic characterization and phylogenetic data support the placement of isolates C23T and HBSQ001 in a novel species in a new genus within the Halobacteriaceae, for which we propose the name Haloquadratum walsbyi gen. nov., sp. nov. The type strain of Haloquadratum walsbyi is C23T (=JCM 12705T=DSM 16854T)., Strains C23T and HBSQ001 were isolated from solar salterns and are novel square-shaped, aerobic, extremely halophilic members of the domain Archaea and family Halobacteriaceae. Cells stained Gram-negative and grew optimally in media containing 18 % salts at around neutral pH. Mg2+ is not required. The DNA G+C content of both isolates was 46.9 mol% and DNA–DNA cross-hybridization showed a relatedness of 80 %. Their 16S rRNA gene sequences showed only 2 nucleotide differences (99.9 % identity) and phylogenetic tree reconstructions with other recognized members of the Halobacteriaceae indicated that they formed a distinct clade, with the closest relative being Halogeometricum borinquense PR 3T (91.2 % sequence identity). The major polar glycolipid of both isolates was the sulfated diglycosyl diether lipid S-DGD-1. Electron cryomicrosopy of whole cells revealed similar internal structures, such as gas vesicles and polyhydroxyalkanoate granules, but the cell wall of isolate HBSQ001 displayed a more complex S-layer compared with that of isolate C23T. The phenotypic characterization and phylogenetic data support the placement of isolates C23T and HBSQ001 in a novel species in a new genus within the Halobacteriaceae, for which we propose the name Haloquadratum walsbyi gen. nov., sp. nov. The type strain of Haloquadratum walsbyi is C23T (=JCM 12705T=DSM 16854T).

Civil Engineering and Geosciences