Environmental drivers define contrasting microbial habitats in first spatial survey of Lake Baikal

Session: 15. - Environmental 'omics: New Tools for Aquatic Ecosystem Science and Management

Paul Wilburn, W.K. Kellogg Biological Station, Michigan State University, [email protected]
Kirill Shchapov, Large Lakes Observatory, [email protected]
Elena Litchman, W.K. Kellogg Biological Station, Michigan State University, [email protected]

Abstract

Resolving drivers of diversity and identifying habitats are central to microbial ecology. We present the first 16s survey with spatial and depth coverage of Lake Baikal across 46 sites during summer stratification. Going beyond description of taxonomic makeup, co-occurrence networks identified two significant OTU clusters, suggesting the lake supported two main microbial habitats. Eigen analysis of member OTUs within each cluster revealed opposing correlations with depth and temperature, indicating that clusters reflected the upper mixed layer (ML) and deeper waters (DW). We modeled OTU richness and Shannon diversity in the two habitats using exhaustive multiple linear regression and model averaging. The extremes of each habitat (warm eutrophic in ML and coldest DW) showed higher Shannon diversity. However, diversity in the mixed layer was best predicted by temperature and achieved through OTU richness, while DW diversity was predicted by depth, elevated by OTU evenness. In both environments, nutrients (DS in ML, TP in DW) contributed to an increase in OTU richness but with dominance of opportunistic OTUs. Furthermore, OTU makeup of the two habitats showed a significant phylogenetic signal with known taxonomic traits reflecting habitat conditions. Our approach showcases bioinformatic, statistical, and phylogenetic approaches to resolving microorganism biology across habitat niches.

1. Keyword
habitats

2. Keyword
mathematical models

3. Keyword
microbiological studies

4. Additional Keyword
bioinformatics

5. Additional Keyword
network analyses

6. Additional Keyword
phylogenetics