| Popis: |
Next-generation sequencing (NGS) and meta-barcoding approaches have revolutionized understanding of within-host communities, such as the gut microbiome, in humans and laboratory animals. The application of such approaches in wild animal populations is growing, but there is a disconnect between the widely-applied generalised linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology which is typically applied to meta-barcoding data.Here, we describe and illustrate a novel GLMM-based approach for analysing the taxon-specific sequence read counts derived from standard meta-barcoding data. This approach allows us to decompose the contribution of different drivers of variation in community structure (e.g. year, season, individual host), via interaction terms in the random effects structure of the model. We also show how these models can be used to determine the degree to which specific taxa or taxonomic groups are responsible for variance attributed to different drivers.To illustrate this approach, we applied it to two cross-sectional meta-barcoding data sets from the Soay Sheep population of St. Kilda. The GLMM approach yielded results that were in agreement with more classical approaches from community ecology, showing that variation the gut microbiota community in these sheep was better explained by age group than by season. We were able to quantify the contributions of different sources of variation to community structure, and also to drill down into the model predictions to show that the age effects we observed were principally due to increases in taxa of the phyla Bacteroidetes and declines in taxa of the phyla Firmicutes.Our proposed models offer a powerful new approach to understanding the drivers of variation in estimates of community structure derived from meta-barcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate that contribution of host genotype, environment, and microbial/parasite phylogeny to observed community structure, and thus provide a powerful means to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. |
