Nutrient recycling by fish in streams along a gradient of agricultural land use
Bibliographic record
Abstract
Nutrient recycling is an essential ecosystem process provided by animals. In many aquatic systems, fish have been identified as important in ecosystem nutrient recycling; however, this importance can vary widely between systems. The factors controlling intersystem variation in animal-mediated nutrient cycling have rarely been examined and as such it remains unclear what impact human landscape changes will have upon these processes. Here we examined rates of nutrient recycling for temperate stream fish assemblages along a gradient of agricultural land use (proportion cropland in the watershed: 1–59%). We quantified nutrient excretion rates of both ammonium–N (NH4+–N) and phosphate (as soluble reactive phosphate: SRP) for fish assemblages at eight streams in southern Ontario, Canada with species-specific excretion measurements and quantitative assemblage sampling. For both nutrients, total assemblage excretion exhibited a strong positive relationship with riparian cropland. The distance required for fish assemblages to turn over ambient nutrient pools was shorter for cropland systems, indicating that the relative importance of excreted nutrients was higher in these systems. Based on measured uptake rates of NH4+–N in two streams (one higher cropland and one low cropland) and on modeled uptake rates for all streams, the proportion of ecosystem demand that can be satisfied by excretion is generally higher in the more agricultural streams. These patterns appear to be driven largely by disproportionate increases in fish assemblage biomass with increasing stream nutrient concentrations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".