Algae alleviate carbon limitation of heterotrophic bacteria in a boreal peatland
Bibliographic record
Abstract
Summary In most high‐latitude wetlands, carbon accumulation as peat represents a balance between plant net primary productivity and heterotrophic decomposition. We hypothesized that this assessment of ecosystem metabolism is incomplete as it does not include information on energy inputs from microalgae, which form complex biofilms with heterotrophic micro‐organisms on the peat surface. To investigate the potential for algae (and associated exudates) to support heterotrophic metabolism under ambient and elevated nutrient levels, we conducted a factorial enrichment of nutrients (nitrogen and phosphorus) and carbon (glucose) in mesocosms with and without the presence of algae (using light‐transparent and dark treatments, respectively) in an Alaskan fen. We measured respiration rates and changes in bacterial biomass to characterize the response of heterotrophic bacteria to our experimental treatments. During incubation assays, algae released up to 38% of their net productivity as exudates and there was a positive relationship between algal production and concentrations of dissolved organic carbon inside mesocosms. Elevated algal productivity in the presence of nutrient enrichment stimulated heterotrophic bacterial respiration and biomass. These responses did not occur with nutrient enrichment in the dark (without algae). The response of bacteria to algae was similar in magnitude to bacterial responses to glucose enrichment. Synthesis. We conclude that bacteria in this boreal fen were primarily limited by labile carbon, and this constraint was alleviated in the presence of elevated algal production. Consequently, algae may facilitate hotspots of microbial activity in northern peatlands, especially in conditions of greater nutrient availability associated with more variable hydrology expected for this region with ongoing climate change.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".