Fish supply distinct nutrients and dissolved organic matter composition relative to ambient concentrations in northern lakes
Bibliographic record
Abstract
Abstract Nutrient cycling in aquatic ecosystems is often facilitated by consumers through excretion. While fish play established roles in nitrogen (N) and phosphorus (P) cycling, their impact on carbon (C) cycling remains underexplored. This role could be significant in ecosystems where dissolved organic matter (DOM) regulates light and nutrient availability. However, the influence of DOM concentration (often measured as dissolved organic carbon [DOC]) and composition on biological processes is complex, with organisms showing diverse responses, sometimes leading to nonlinear patterns. We investigated fish excretion of N and P in 11 boreal lakes with varying DOC concentration and DOM composition and assessed fish excretion of DOC and DOM composition in a subset of three of those lakes representing low, medium, and high DOC levels. In line with our expectations, fish mass‐normalized N excretion rates had an inverse unimodal relationship with ambient DOC up to 7.3 mg C L−1, yet with little evidence supporting a relationship between fish P excretion rates and ambient DOC. Fish exhibited lower DOC excretion rates in medium DOC environments compared to high DOC environments and excreted more terrestrially‐ and microbially‐derived DOM in low DOC environments. Overall, our findings underscore the dynamic role of fish as conduits of nutrients and carbon in aquatic ecosystems, shaped by ambient nutrients and light conditions, and physiological processes that warrant further investigation.
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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) | 0.001 | 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".