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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 Recent advances in sequencing technology and bioinformatic pipelines have allowed unprecedented access to the genomes of yet‐uncultivated microorganisms from diverse environments. However, the catalogue of freshwater genomes remains limited, and most genome recovery attempts in freshwater ecosystems have only targeted specific taxa. Here, we present a genome recovery pipeline incorporating iterative subtractive binning, and apply it to a time series of 100 metagenomic datasets from seven connected lakes and estuaries along the Chattahoochee River (Southeastern USA). Our set of metagenome‐assembled genomes (MAGs) represents >400 yet‐unnamed genomospecies, substantially increasing the number of high‐quality MAGs from freshwater lakes. We propose names for two novel species: ‘CandidatusElulimicrobium humile’ (‘Ca. Elulimicrobiota’, ‘Patescibacteria’) and ‘CandidatusAquidulcis frankliniae’ (‘Chloroflexi’). Collectively, our MAGs represented about half of the total microbial community at any sampling point. To evaluate the prevalence of these genomospecies in the chronoseries, we introduce methodologies to estimate relative abundance and habitat preference that control for uneven genome quality and sample representation. We demonstrate high degrees of habitat‐specialization and endemicity for most genomospecies in the Chattahoochee lakes. Wider ecological ranges characterized smaller genomes with higher coding densities, indicating an overall advantage of smaller, more compact genomes for cosmopolitan distributions.