Natural land cover in agricultural catchments alters flood effects on DOM composition and decreases nutrient levels in streams
Bibliographic record
Abstract
A shift in natural hydrologic patterns, such as increases in the frequency, and changes in the magnitude of flood events are expected with climate change. A better understanding of how land use and hydrological patterns interact to affect solute levels in aquatic systems is needed so we can better navigate expected climatic changes. Here we analyzed spatiotemporal event-based data from 21 predominantly agricultural catchments with varying contributions of natural land cover. We studied the effect of hydrological events on stream dissolved phosphorus and nitrogen concentrations and dissolved organic matter (DOM) composition and bioavailability over 4 years. Our results suggest that flow regime and flood condition control stream DOM composition, nitrogen and phosphorus dynamics, modulated by seasonal processes and land use properties, like soil organic carbon content. Although higher flows generally increased solute concentrations as well as the fraction of terrestrial, humic-like DOM, this pattern was highly dependent on the catchment land use and event timing. General additive models indicated a threshold of about 30–40% natural land cover, below which DOC and nutrients showed a positive relationship with discharge, but when more than 30–40% natural features (for example, wetlands, woodlots and grasslands) were present in the catchments, this shifted to a negative relationship. This suggests that in agricultural landscapes, the presence of natural land cover is important as it can decrease solute concentrations in streams and may act as a buffer, mitigating the effect of floods on DOM and nutrient export rates.
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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".