Contribution of different dispersal sources to the metabolic response of lake bacterioplankton following a salinity change
Bibliographic record
Abstract
Summary Dispersal can modify how bacterial community composition (BCC) changes in response to environmental perturbations, yet knowledge about the functional consequences of dispersal is limited. Here we hypothesized that changes in bacterial community production in response to a salinity disturbance depend on the possibility to recruit cells from different dispersal sources. To investigate this, we conducted an in situ mesocosm experiment where bacterial communities of an oligotrophic lake were exposed to different salinities (0, 18, 36 psu) for 2 weeks and subjected to dispersal of cells originating from sediments, air (mesocosms open to air deposition), both or none. BCC was determined using 454 pyrosequencing of the 16S rRNA gene and bacterial production was measured by 3 H leucine uptake. Bacterial production differed significantly among salinity treatments and dispersal treatments, being highest at high salinity. These changes were associated with changes in BCC and it was found that the identity of the main functional contributors differed at different salinities. Our results further showed that after a salinity perturbation, the response of bacterial communities depended on the recruitment of taxa, including marine representatives (e.g., Alphaproteobacteria Loktanella , Erythrobacter and the Gammaproteobacterium Rheiheimera ) from dispersal sources, in which atmospheric deposition appeared to play a major role.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".