Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean
Bibliographic record
Abstract
Microbial taxa range from being ubiquitous and abundant across space to extremely rare and endemic, depending on their ecophysiology and on different processes acting locally or regionally. However, little is known about how cosmopolitan or rare taxa combine to constitute communities and whether environmental variations promote changes in their relative abundances. Here we identified the Spatial Abundance Distribution (SpAD) of individual prokaryotic taxa (16S rDNA-defined Operational Taxonomic Units, OTUs) across 108 globally-distributed surface ocean stations. We grouped taxa based on their SpAD shape ("normal-like"- abundant and ubiquitous; "logistic"- globally rare, present in few sites; and "bimodal"- abundant only in certain oceanic regions), and investigated how the abundance of these three categories relates to environmental gradients. Most surface assemblages were numerically dominated by a few cosmopolitan "normal-like" OTUs, yet there was a gradual shift towards assemblages dominated by "logistic" taxa in specific areas with productivity and temperature differing the most from the average conditions in the sampled stations. When we performed the SpAD categorization including additional habitats (deeper layers and particles of varying sizes), the SpAD of many OTUs changed towards fewer "normal-like" shapes, and OTUs categorized as globally rare in the surface ocean became abundant. This suggests that understanding the mechanisms behind microbial rarity and dominance requires expanding the context of study beyond local communities and single habitats. We show that marine bacterial communities comprise taxa displaying a continuum of SpADs, and that variations in their abundances can be linked to habitat transitions or barriers that delimit the distribution of community members.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".