Evolution of Divergent Life History Strategies in Marine Alphaproteobacteria
Bibliographic record
Abstract
UNLABELLED: Marine bacteria in the Roseobacter and SAR11 lineages successfully exploit the ocean habitat, together accounting for ~40% of bacteria in surface waters, yet have divergent life histories that exemplify patch-adapted versus free-living ecological roles. Here, we use a phylogenetic birth-and-death model to understand how genome content supporting different life history strategies evolved in these related alphaproteobacterial taxa, showing that the streamlined genomes of free-living SAR11 were gradually downsized from a common ancestral genome only slightly larger than the extant members (~2,000 genes), while the larger and variably sized genomes of roseobacters evolved along dynamic pathways from a sizeable common ancestor (~8,000 genes). Genome changes in the SAR11 lineage occurred gradually over ~800 million years, whereas Roseobacter genomes underwent more substantial modifications, including major periods of expansion, over ~260 million years. The timing of the first Roseobacter genome expansion was coincident with the predicted radiation of modern marine eukaryotic phytoplankton of sufficient size to create nutrient-enriched microzones and is consistent with present-day ecological associations between these microbial groups. We suggest that diversification of red-lineage phytoplankton is an important driver of divergent life history strategies among the heterotrophic bacterioplankton taxa that dominate the present-day ocean. IMPORTANCE: One-half of global primary production occurs in the oceans, and more than half of this is processed by heterotrophic bacterioplankton through the marine microbial food web. The diversity of life history strategies that characterize different bacterioplankton taxa is an important subject, since the locations and mechanisms whereby bacteria interact with seawater organic matter has effects on microbial growth rates, metabolic pathways, and growth efficiencies, and these in turn affect rates of carbon mineralization to the atmosphere and sequestration into the deep sea. Understanding the evolutionary origins of the ecological strategies that underlie biochemical interactions of bacteria with the ocean system, and which scale up to affect globally important biogeochemical processes, will improve understanding of how microbial diversity is maintained and enable useful predictions about microbial response in the future ocean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".