Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population's mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment.

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Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population's mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely-or at least for a long time-even in a constant environment.

The pelagophyte Aureococcus anophagefferens causes harmful brown tide blooms in marine embayments on three continents. Aureococcus anophagefferens was the first harmful algal bloom species to have its genome sequenced, an advance that evidenced genes important for adaptation to environmental conditions that prevail during brown tides. To expand the genomic tools available for this species, genomes for four strains were assembled, including three newly sequenced strains and one assembled from publicly available data. These genomes ranged from 57.11 to 73.62 Mb, encoding 13,191–17,404 potential proteins. All strains shared ~90% of their encoded proteins as determined by homology searches and shared most functional orthologs as determined by KEGG, although each strain also possessed coding sequences with unique functions. Like the original reference genome, the genomes assembled in this study possessed genes hypothesized to be important in bloom proliferation, including genes involved in organic compound metabolism and growth at low light. Cross‐strain informatics and culture experiments suggest that the utilization of purines is a potentially important source of organic nitrogen for brown tides. Analyses of metatranscriptomes from a brown tide event demonstrated that use of a single genome yielded a lower read mapping percentage (~30% of library reads) as compared to a database generated from all available genomes (~43%), suggesting novel information about bloom ecology can be gained from expanding genomic space. This work demonstrates the continued need to sequence ecologically relevant algae to understand the genomic potential and their ecology in the environment.