Predator-Driven Nutrient Recycling in California Stream Ecosystems
Bibliographic record
Abstract
Nutrient recycling by consumers in streams can influence ecosystem nutrient availability and the assemblage and growth of photoautotrophs. Stream fishes can play a large role in nutrient recycling, but contributions by other vertebrates to overall recycling rates remain poorly studied. In tributaries of the Pacific Northwest, coastal giant salamanders (Dicamptodon tenebrosus) occur at high densities alongside steelhead trout (Oncorhynchus mykiss) and are top aquatic predators. We surveyed the density and body size distributions of D. tenebrosus and O. mykiss in a California tributary stream, combined with a field study to determine mass-specific excretion rates of ammonium (N) and total dissolved phosphorus (P) for D. tenebrosus. We estimated O. mykiss excretion rates (N, P) by bioenergetics using field-collected data on the nutrient composition of O. mykiss diets from the same system. Despite lower abundance, D. tenebrosus biomass was 2.5 times higher than O. mykiss. Mass-specific excretion summed over 170 m of stream revealed that O. mykiss recycle 1.7 times more N, and 1.2 times more P than D. tenebrosus, and had a higher N:P ratio (8.7) than that of D. tenebrosus (6.0), or the two species combined (7.5). Through simulated trade-offs in biomass, we estimate that shifts from salamander biomass toward fish biomass have the potential to ease nutrient limitation in forested tributary streams. These results suggest that natural and anthropogenic heterogeneity in the relative abundance of these vertebrates and variation in the uptake rates across river networks can affect broad-scale patterns of nutrient limitation.
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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 | 0.001 | 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".