Herbivory by an invasive snail increases nitrogen fixation in a nitrogen-limited stream
Bibliographic record
Abstract
Despite anthropogenic nitrogen contributions, nitrogen fixation contributes half of biosphere inputs but has rarely been quantified in streams. Herbivory controls algal biomass and productivity in streams, and we hypothesized that herbivory could also control nitrogen fixation. We released periphyton from herbivory in nitrogen-limited Polecat Creek, Wyoming, where heavy grazing by the invasive New Zealand mudsnail ( Potamopyrgus antipodarum ) dominates nitrogen cycling. One and two weeks after releasing periphyton, we found higher rates of nitrogen fixation on heavily grazed rocks (two-way analysis of variance (ANOVA), p = 0.012). Time elapsed after snail manipulation had no effect (two-way ANOVA, p = 0.24). Grazing changed periphyton composition by reducing the proportion of green algae and increasing the proportion of nitrogen-fixing diatoms (multivariate ANOVA, p = 0.001). Nitrogen fixation rates increased disproportionately to nitrogen-fixing algal cells, indicating that snails increased nitrogenase efficiency, probably by improving light and (or) nutrient availability to nitrogen fixers. We incorporated our nitrogen fixation rates into a published nitrogen budget for Polecat Creek and found that nitrogen flux into the periphyton was 50% higher when we included nitrogen fixation. Herbivory can increase nitrogen fixation in streams, and future studies should measure nitrogen fixation for a more thorough understanding of stream nitrogen cycling.
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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 | 0.000 |
| 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) | 0.001 | 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".