Comparative Analysis of the Fecal Microbiota of Relict Gull (Larus relictus) in Mu Us Desert (Hao Tongcha Nur) and Bojiang Haizi in Inner Mongolia, China

Abstract

The gut microbiota contributes to host health by improving digestive efficiency and maintaining homeostasis. The relict gull (Larus relictus), a national first-class protected bird in China, is listed as vulnerable in the International Union for Conservation of Nature Red List. Here, 16S rRNA gene sequencing was performed to characterize and compare the community composition and diversity of the gut microbiota sampled from relict gulls in two breeding sites. In total, 418 operational taxonomic units (OUTs) were obtained and classified into 15 phyla and 228 genera. Alpha diversity analysis revealed no significant differences in community diversity among the two breeding sites. Beta diversity analyses showed that the microbial communities at the two sites were different. Six dominant phyla and fourteen dominant genera were identified. The most abundant bacterial genera had a significant relationship with the diet and living environment, and some bacterial genera were found to adapt to the plateau environment in which relict gulls live, which enables the relict gulls to use local resources effectively to accumulate energy. Simultaneously, a variety of highly abundant pathogenic bacteria were found, suggesting that these gulls may spread diseases among the local gull population. Certain measures should be taken to protect this species and to prevent the spread of diseases.

Keywords: 16S rRNA gene; Bojiang Haizi; Mu Us Desert; gut microbiota; high-throughput sequencing; relict gulls.

Figure 1

The Rarefaction curve (A) and Shannon diversity curve (B) of the bacterial community in the analyzed samples. YOBJ, relict gull (Larus relictus) of Bojiang Haizi; YOKX, relict gull (Larus relictus) of Mu Us Desert.

Figure 2

Venn diagram of the bacterial community in the analyzed groups. It shows the numbers of shared or not shared operational taxonomic units (OTUs; 97% sequence identity) (A), phyla (B), and genera (C) by Mu Us Desert (KX) and Bojiang Haizi (BJ) individuals depending of overlaps.

Figure 3

Abundance-based coverage estimator (ACE) index (A), Chao1 diversity (B), Shannon diversity (C), and Simpson indices (D) of bacterial populations in each sample. BJ, Bojiang Haizi; KX, Mu Us Desert.

Figure 4

Non-metric multidimensional scaling (NMDS) analysis. Each point represented one sample. The distance between points represented the degree of difference. The closer the distance between the samples in the graph, the higher the similarity between them. BJ, Bojiang Haizi; KX, Mu Us Desert.

Figure 5

Bar graph of the relative bacterial abundance at phylum (A) and genus (B) levels. The Bacterial those with relative abundance (%) over 1% were shown. Others are bacterial with a relative abundance of <1%. BJ, Bojiang Haizi; KX, Mu Us Desert.

Figure 6

Linear discriminant analysis (LDA) effect size (LEfSe) analysis. (A) The microbial species had significant differences in the two breeding sites. Different colors presents different groups, the species classification at the level of genus, family, order, class, and phylum were exhibited from the outside to the inside. (B) The plot from LEfSe analysis. The length of the bar column represents the LDA score. The figure exhibited the microbial with significant differences between the two breeding grounds (LDA score > 4.0).

Figure 7

Bacterial community functional prediction by Kyoto Encyclopedia of Genes and Genomes (KEGG) database.