Magnitude and regulation of bacterioplankton respiratory quotient across freshwater environmental gradients
Bibliographic record
Abstract
Bacterioplankton respiration (BR) may represent the largest single sink of organic carbon in the biosphere and constitutes an important driver of atmospheric carbon dioxide (CO(2)) emissions from freshwaters. Complete understanding of BR is precluded by the fact that most studies need to assume a respiratory quotient (RQ; mole of CO(2) produced per mole of O(2) consumed) to calculate rates of BR. Many studies have, without clear support, assumed a fixed RQ around 1. Here we present 72 direct measurements of bacterioplankton RQ that we carried out in epilimnetic samples of 52 freshwater sites in Québec (Canada), using O(2) and CO(2) optic sensors. The RQs tended to converge around 1.2, but showed large variability (s.d.=0.45) and significant correlations with major gradients of ecosystem-level, substrate-level and bacterial community-level characteristics. Experiments with natural bacterioplankton using different single substrates suggested that RQ is intimately linked to the elemental composition of the respired compounds. RQs were on average low in net autotrophic systems, where bacteria likely were utilizing mainly reduced substrates, whereas we found evidence that the dominance of highly oxidized substrates, for example, organic acids formed by photo-chemical processes, led to high RQ in the more heterotrophic systems. Further, we suggest that BR contributes to a substantially larger share of freshwater CO(2) emissions than presently believed based on the assumption that RQ is ∼1. Our study demonstrates that bacterioplankton RQ is not only a practical aspect of BR determination, but also a major ecosystem state variable that provides unique information about aquatic ecosystem functioning.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".