Stable isotope analyses on archived fish scales reveal the long‐term effect of nitrogen loads on carbon cycling in rivers
Bibliographic record
Abstract
Stable isotope analysis of organic matter in sediment records has long been used to track historical changes in productivity and carbon cycling in marine and lacustrine ecosystems. While flow dynamics preclude stratigraphic measurements of riverine sediments, such retrospective analysis is important for understanding biogeochemical cycling in running waters. Unique collections of riverine fish scales were used to analyse δ(15) N and δ(13) C variations in the food web of two European rivers that experience different degrees of anthropogenic pressure. Over the past four decades, dissolved inorganic N loading remained low and constant in the Teno River (70°N, Finland); in contrast, N loading increased fourfold in the Scorff River (47°N, France) over the same period. Archived scales of Atlantic salmon parr, a riverine life-stage that feeds on aquatic invertebrates, revealed high δ(15) N values in the Scorff River reflecting anthropogenic N inputs to that riverine environment. A strong correlation between dissolved inorganic N loads and δ(13) C values in fish scales was observed in the Scorff River, whereas no trend was found in the Teno River. This result suggests that anthropogenic N-nutrients enhanced atmospheric C uptake by primary producers and its transfer to fish. Our results illustrate for the first time that, as for lakes and marine ecosystems, historical changes in anthropogenic N loading can affect C cycling in riverine food webs, and confirm the long-term interactions between N and C biogeochemical cycles in running waters.
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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 | 0.001 |
| 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".