Composition Influences the Pathway but not the Outcome of the Metabolic Response of Bacterioplankton to Resource Shifts
Bibliographic record
Abstract
Bacterioplankton community metabolism is central to the functioning of aquatic ecosystems, and strongly reactive to changes in the environment, yet the processes underlying this response remain unclear. Here we explore the role that community composition plays in shaping the bacterial metabolic response to resource gradients that occur along aquatic ecotones in a complex watershed in Québec. Our results show that the response is mediated by complex shifts in community structure, and structural equation analysis confirmed two main pathways, one involving adjustments in the level of activity of existing phylotypes, and the other the replacement of the dominant phylotypes. These contrasting response pathways were not determined by the type or the intensity of the gradients involved, as we had hypothesized, but rather it would appear that some compositional configurations may be intrinsically more plastic than others. Our results suggest that community composition determines this overall level of community plasticity, but that composition itself may be driven by factors independent of the environmental gradients themselves, such that the response of bacterial communities to a given type of gradient may alternate between the adjustment and replacement pathways. We conclude that community composition influences the pathways of response in these bacterial communities, but not the metabolic outcome itself, which is driven by the environment, and which can be attained through multiple alternative configurations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".