Decadal Changes in Anthropogenic Inputs and Precipitation Influence Riverine Exports of Carbon, Nitrogen, and Phosphorus, and Alter Ecosystem Level Stoichiometry
Bibliographic record
Abstract
Abstract Changes in precipitation and land use influence carbon (C), nitrogen (N), and phosphorus (P) exports from land to receiving waters. However, how these drivers differentially alter elemental inputs and impact subsequent ecosystem stoichiometry over time remains poorly understood. Here, we quantified long‐term (1979–2020) trends in C, N, and P exports at three sites along the mainstem of a north temperate river in Québec, Canada, that successively drains forested, urban, and more agriculturally impacted land‐use areas. Riverine N and to a lesser degree C exports tended to increase over time, with major inter‐annual variation largely resolved by changes in precipitation. Historical increases in net anthropogenic N inputs on land (NANI) also explained increases in riverine N exports, with about 35% of NANI reaching the river annually. Despite higher Net anthropogenic P inputs, NAPI, over time, P exports tended to decrease at all riverine sites. This decrease in P at the forested site was more gradual, whereas a precipitous drop was observed at the downstream urban site following legislated P removal in municipal wastewater. Changes in historical ecosystem stoichiometry reflected the differential elemental exports due to natural and anthropogenic drivers and ranged from 174: 23: 1 to 547: 76: 1 over the years. Our work shows how C, N, and P have responded to different drivers in the same catchment over the last four decades, and how their differential riverine exports have influenced ecosystem stoichiometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".