Prairie stream nutrient stoichiometry across space and time: Influence of discharge, season, and crop type
Bibliographic record
Abstract
Abstract Stream water nutrient ratios are often influenced by flow variation and landscape characteristics. However, the influence of these drivers on total and dissolved nutrient ratios remains understudied, especially in prairie ecosystems where hydrologic connectivity between soils and streams exhibits substantial spatial and seasonal variability. Here, we ask how hydrology and land cover drive patterns of nitrogen (N), phosphorus (P), and N : P ratios across streams and rivers draining northern prairie ecosystems. To answer this, we compiled nutrient concentration data for tributaries of the Red River, Manitoba, Canada, to assess seasonal and annual variation in nutrient ratios, as well as the relationship between crop cover, discharge, and ratios, over 1‐yr, 10‐yr, and 30‐yr time spans. Total nitrogen : total phosphorus ratios were near the Redfield mass ratio (N/P = 7.23) across 24 streams in the Red River Valley. By comparison, dissolved inorganic nitrogen : total dissolved phosphorus ratios in these streams were N depleted and generally declined from spring through to autumn. The types of crops grown did not appear to be a consistent influence on nutrient ratios in streams throughout the region. In contrast, stream flows strongly influenced spring and summer nutrient ratios in four tributaries over a 30‐yr period. Specifically, increasing stream flow tended to decrease TN : TP and DIN : TDP in the two eastern tributaries but increase DIN : TDP in the western tributaries. Our findings that nutrient ratios in prairie streams are impacted by seasonality and fluctuating hydrologic conditions suggest that nutrient ratios in Red River tributaries may be impacted by future climate change.
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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 | 0.000 | 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".