Development and evaluation of an ensemble model to identify host-related metadata from fecal microbiota of zoo-housed mammals

Abstract

Much research has been conducted to describe the factors that determine the fecal microbiome, with diet and host phylogeny as the main drivers. The influence of diet has been described at different levels. Firstly, there are major differences in the microbiomes of herbivorous and carnivorous species and secondly the morphology of the digestive system also determines the composition and diversity of the microbiota. In this study, we aim to describe the influence of the three factors – diet, digestive system and host - on the microbiota in order to develop a model that is able to characterize host-specific metadata from an unknown fecal sample. We therefore analyzed the 16s rRNA from 525 fecal samples of 14 zoo-housed species belonging to different phylogenetic groups including herbivores, carnivores and omnivores. We found significant differences in the bacterial taxa correlated with these groups. While herbivores show positive correlations with a large number of bacterial taxa, we found fewer taxa correlating with carnivores or omnivores. We also detected considerable differences in the microbiota of the ruminant, hindgut fermenting and simple digestive system. Based on these results, we developed a logistic ensemble model, that predicts the diet and based on these findings either the herbivorous digestive system or the carnivorous host-family from a given fecal microbiota composition. This model is able to effectively discriminate herbivores, omnivores and carnivores. It also excels at predicting the herbivore-specific digestive system with 98% accuracy, further reinforcing the strong link between microbiota and digestive system morphology. Carnivorous host-family identification achieves an overall accuracy of 79%, although this performance varies between families. We provide this trained model as a tool to enable users to generate host-specific information from their microbiome data. In future research, tools such as the one presented here could lead to a combined approach of microbiome and host-specific analyses which would be a great advantage in non-invasive wildlife monitoring.