Direct and indirect effects of fish on pelagic nitrogen and phosphorus availability in oligotrophic Arctic Alaskan lakes
Bibliographic record
Abstract
The importance of fish nutrient recycling for lake primary production increases with lake productivity. However, fish in low-productivity lakes may have substantial indirect effects on nutrient recycling from lower trophic levels. We measured nutrient excretion rates from fish and zooplankton in oligotrophic Arctic lakes and investigated direct and indirect fish effects on consumer nutrient recycling. Fish nutrient excretion rates were small relative to phytoplankton nutrient demand. Zooplankton excretion, however, supplied 19%–130% and 37%–200% of phytoplankton nitrogen and phosphorus demand, respectively. Fish had a significant effect on zooplankton biomass; in lakes with fish, this was approximately 80% lower than in lakes without fish. The difference in zooplankton biomass was due primarily to a decrease in zooplankton density; no significant difference in average zooplankton size was observed between fish and fishless lakes. Fish also impacted zooplankton community composition; communities in lakes with fish were dominated by copepods compared with cladoceran dominance in lakes without fish. Because of lower zooplankton biomass, lakes with fish showed lower rates of zooplankton nitrogen and phosphorus excretion relative to lakes without fish. Thus, our results support the hypothesis that fish have major indirect effects on lake nutrient cycles, even when direct excretion from fish is minimal.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".