Contrasting response of microeukaryotic and bacterial communities to the interplay of seasonality and local stressors in shallow soda lakes

Abstract

Seasonal environmental variation is a leading driver of microbial planktonic community assembly and interactions. However, departures from usual seasonal trends are often reported. To understand the role of local stressors in modifying seasonal succession, we sampled fortnightly throughout three seasons five nearby shallow soda lakes exposed to identical seasonal and meteorological changes. We characterised their microeukaryotic and bacterial communities by amplicon sequencing of the 16S and 18S rRNA gene, respectively. Biological interactions were inferred by analyses of synchronous and time-shifted interaction networks, and the keystone taxa of the communities were topologically identified. The lakes showed similar succession patterns during the study period with spring being characterised by relevance of trophic interactions and certain level of community stability followed by a more dynamic and variable summer-autumn period. Adaptation to general seasonal changes happened through shared core microbiome of the pans. Stochastic events such as desiccation disrupted common network attributes and introduced shifts from the prevalent seasonal trajectory. Our results demonstrated that despite being extreme and highly variable habitats, shallow soda lakes exhibit certain similarities in seasonality of their planktonic communities, yet local stressors such as droughts instigate deviations from prevalent trends to a greater extent for microeukaryotic than for bacterial communities.

A matter of salt: global assessment of the effect of salt ionic composition as a driver of aquatic bacterial diversity

“…1H), or by the distance decay analyses (Fig. S7) where the similarity between time series samples from the same sampling location was often lower than the similarity of samples from distant sites, reflecting previously reported high seasonal turnover of bacterial communities in these habitats [16, 17]. A likely explanation for the contrasting results from the PERMANOVA could be that samples from the same region were often processed within the same project or by the same group and that regional differences were in fact reflecting variations in sampling and sample processing (e.g., sample collection, DNA extraction, PCR amplification) and not true spatial processes such as dispersal limitation.…”