Elevated pH regulates bacterial carbon cycling in lakes with high photosynthetic activity
Bibliographic record
Abstract
Bacteria are critically important for carbon (C) cycling and energy flow in aquatic environments. However, studies to date have largely focused on the role of substrate quality in the regulation of this important process. As such, we know little about the role of other ecological drivers in shaping bacterially mediated C cycling. Here we examine the manner in which planktonic bacterial abundance (BA), productivity (BP), respiration (BR), and growth efficiency (BGE), and thus C cycling are affected by elevated pH, an ecological factor that occurs commonly in highly productive aquatic systems. We undertook our study in lakes of the Mackenzie Delta region of Canada. These lakes routinely experience high pH caused by rapid macrophyte photosynthesis. Two different experiment types were employed: first, a series of short-term experiments was used to assess the direct effects of elevated pH on bacteria experiencing differing pH levels in situ. Second, long-term mesocosms were used to explore the effect of elevated pH on bacteria over longer time scales and in the presence of other trophic levels. Bacterial productivity and BR slowed dramatically with elevated pH over the short term, potentially uncoupling bacterial processing of organic matter from its in-lake production and causing a switch away from biomass creation and toward C mineralization. With longer term exposure, bacterial communities adapted to the direct stress of elevated pH, but responses at higher trophic levels caused a cascade that mediated the effect of alkalization on bacteria, in a manner that could well vary among aquatic ecosystems. Our study establishes elevated pH as a key driver of bacterial C cycling and energy flow in aquatic systems with high autotrophic productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".