Soils associated to different tree communities do not elicit predictable responses in lake bacterial community structure and function
Bibliographic record
Abstract
Freshwater bacterioplankton communities are influenced by the inputs of material and bacteria from the surrounding landscape, yet few studies have investigated how different terrestrial inputs affect bacterioplankton. We examined whether the addition of soils collected under various tree species combinations differentially influences lake bacterial communities. Lake water was incubated for 6 days following addition of five different soils. We assessed the taxonomic composition (16S rRNA gene sequencing) and metabolic activity (Biolog Ecoplates) of lake bacteria with and without soil addition, and compared these to initial soil communities. Soil bacterial assemblages showed a strong influence of tree composition, but such community differences were not reflected in the structure of lake communities that developed during the experiment. Bacterial taxa showing the largest abundance increases during incubation were initially present in both lake water and across most soils, and were related to Cytophagales, Burkholderiales and Rhizobiales. No clear metabolic profiles based on inoculum source were found, yet soil-amended communities used 60% more substrate than non-inoculated communities. Overall, we show that terrestrial inputs influence aquatic communities by stimulating the growth and activity of certain ubiquitous taxa distributed across the terrestrial-aquatic continuum, yet different forest soils did not cause predictable changes in lake bacterioplankton assemblages.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".