Discrete groundwater inflows influence patterns of nitrogen uptake in a boreal headwater stream
Bibliographic record
Abstract
Dissolved organic carbon (DOC) influences stream nitrogen (N) dynamics by regulating the nutrient demand of heterotrophic microbes and mediating their interactions with nitrifiers. However, DOC supply to streams is dynamic in space and time, which may create variability in N dynamics as a result of shifts between heterotrophic and chemoautotrophic influences. To test this, we measured spatial and temporal variation in concentrations and net uptake of dissolved organic nitrogen (DON), ammonium (NH4+), and nitrate (NO3−) along a 1.4-km boreal stream fed by 4 discrete groundwater inflow zones. We also performed constant rate additions of NH4+, with and without acetate, to test the influence of labile DOC availability on N cycling. Groundwater N supply did not drive spatial patterns in N concentrations. However, we observed high rates of net NH4+ uptake at the sub-reach with the greatest groundwater DOC inputs, whereas net nitrification occurred where such inputs were negligible. At the reach scale, net DON and NH4+ uptake increased with greater groundwater discharge, DOC∶DIN, and ecosystem respiration, whereas net nitrification increased with greater DOC aromaticity. Finally, constant rate additions showed that, under increased DOC availability, NH4+ uptake increased 2×, whereas the proportion of NH4+ nitrified decreased from 42 to 15%. Together, these observations suggest that nitrification rivals heterotrophic uptake when aromatic DOC promotes heterotrophic carbon limitation. Discrete groundwater inflows and periods of elevated discharge can partially alleviate this limitation by supplying labile DOC from riparian soils. Hence, accounting for these land–water connections, over both time and space, is critical for understanding N dynamics in boreal streams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".