Search for: All records

Abstract Understanding how populations respond to disturbances represents a major goal for microbial ecology. While several hypotheses have been advanced to explain microbial community compositional changes in response to disturbance, appropriate data to test these hypotheses is scarce, due to the challenges in delineating rare vs. abundant taxa and generalists vs. specialists, a prerequisite for testing the theories. Here, we operationally define these two key concepts by employing the patterns of coverage of a (target) genome by a metagenome to identify rare populations, and by borrowing the proportional similarity index from macroecology to identify generalists. We applied these concepts to time-series (field) metagenomes from the Piver’s Island Coastal Observatory to establish that coastal microbial communities are resilient to major perturbations such as tropical cyclones and (uncommon) cold or warm temperature events, in part due to the response of rare populations. Therefore, these results provide support for the insurance hypothesis [i.e. the rare biosphere has the buffering capacity to mitigate the effects of disturbance]. Additionally, generalists appear to contribute proportionally more than specialists to community adaptation to perturbations like warming, supporting the disturbance-specialization hypothesis [i.e. disturbance favors generalists]. Several of these findings were also observed in replicated laboratory mesocosms that aimed to simulate disturbances such as a rain-driven washout of microbial cells and a labile organic matter release from a phytoplankton bloom. Taken together, our results advance understanding of the mechanisms governing microbial population dynamics under changing environmental conditions and have implications for ecosystem modeling. 

Summary Disturbances, here defined as events that directly alter microbial community composition, are commonly studied in host‐associated and engineered systems. In spite of global change both altering environmental averages and increasing extreme events, there has been relatively little research into the causes, persistence and population‐level impacts of disturbance in the dynamic coastal ocean. Here, we utilize 3 years of observations from a coastal time series to identify disturbances based on the largest week‐over‐week changes in the microbiome (i.e. identifying disturbance as events that alter the community composition). In general, these microbiome disturbances were not clearly linked to specific environmental factors and responsive taxa largely differed, aside from SAR11, which generally declined. However, several disturbance metagenomes identified increased phage‐associated genes, suggesting that unexplained community shifts might be caused by increased mortality. Furthermore, a category 1 hurricane, the only event that would likely be classifieda priorias an environmental disturbance, was not an outlier in microbiome composition, but did enhance a bloom in seasonally abundant phytoplankton. Thus, as extreme environmental changes intensify, assumptions of what constitutes a disturbance should be re‐examined in the context of ecological history and microbiome responses. 

Summary Recent studies have focused on linking marine microbial communities with environmental factors, yet, relatively little is known about the drivers of microbial community patterns across the complex gradients from the nearshore to open ocean. Here, we examine microbial dynamics in 15 five‐station transects beginning at the estuarine Piver's Island Coastal Observatory (PICO) time‐series site and continuing 87 km across the continental shelf to the oligotrophic waters of the Sargasso Sea. 16S rRNA gene libraries reveal strong clustering by sampling site with distinct nearshore, continental shelf and offshore oceanic communities. Water temperature and distance from shore (which serves as a proxy for gradients in factors such as productivity, terrestrial input and nutrients) both most influence community composition. However, at the phylotype level, modelling shows the distribution of some taxa is linked to temperature, others to distance from shore and some by both factors, highlighting that taxa with distinct environmental preferences underlie apparent clustering by station. Thus, continental margins contain microbial communities that are distinct from those of either the nearshore or the offshore environments and contain mixtures of phylotypes with nearshore or offshore preferences rather than those unique to the shelf environment.