Tracing anthropogenic inputs in stream foods webs with stable carbon and nitrogen isotope systematics along an agricultural gradient
Bibliographic record
Abstract
Stable carbon (13C) and nitrogen isotopes (15N) are useful tools in determining the presence of agricultural influences in freshwater ecosystems. Here we examined δ15N and δ13C signatures in nitrate, fish, and mussel tissues, from rivers in Southern Ontario, Canada, that vary in their catchment proportion of agriculture land use, nutrients and organic matter quality. We found comparatively 15N-enriched δ15N values in animal tissues and dissolved nitrates, relative to expected values characterized by natural sources. We also observed a strong, positive correlation between riparian agriculture percentages and δ15N values in animal tissues and nitrates, indicating a significant influence of agricultural land use and the probable dominance of organic fertilizer and manure inputs in particular. The use of a 15N-based equation for the estimation of fish trophic position confirmed dietary analyses is showing all fish species to be tertiary consumers, with a relatively consistent 15N-enrichment in animal tissues between trophic levels. This indicates that variability in 15N-trophic fractionation is minor, and that fish and mussel tissue δ15N values are largely representative of source nitrogen. However, the trophic fractionation value varied from accepted literature values, suggesting strong influences from either local environmental conditions or dietary variation. The δ13C datasets did not correlate with riparian agriculture, and animal δ13C signatures in their tissues are consistent with terrestrial C3 vegetation, suggesting the dominance of allochthonous DOC sources. We found that changes in water chemistry and dissolved organic matter quality brought about by agricultural inputs were clearly expressed in the δ15N signatures of animal tissues from all trophic levels. As such, this study confirmed the source of anthropogenic nitrogen in the studied watersheds, and demonstrated that this agriculturally-derived nitrogen could be traced with δ15N signatures through successive trophic levels in local aquatic food webs. The δ13C data was less diagnostic of local agriculture, due to the more complex interplay of carbon cycling and environmental conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".