Diet and predation risk affect tissue and excretion nutrients of Trinidadian guppies: a field survey
Bibliographic record
Abstract
Consumers vary in their excretion of nitrogen and phosphorus, altering nutrient cycles and ecosystem function. Traditional mass balance models that focus on dietary and tissue nutrients have poorly explained such variation in excretion. Here, we contrast diet and tissue nutrient models for nutrient excretion with predation risk, an often overlooked factor, using the Trinidadian guppy (Poecilia reticulata) as our model system. We surveyed guppies at 12 sites spread across two streams with parallel gradients in food quality and predation risk. At each site, we assessed guppy diet, tissue nitrogen (N), and phosphorus (P) content, and N and P excretion. Predation risk best explained guppy excretion, especially P: guppies excreted less in sites with a dominant predator, while traditional models for excretion rate based on diet quality and tissue nutrients failed to explain it. Guppy tissue N (but not P) most closely correlated with guppy diet quality, showing evidence for flexible homeostasis. Our work extends previous laboratory studies' results to natural streams and shows that predation risk alters feeding behavior and physiology, driving substantial variation in guppy nutrient, particularly P, excretion rates. We suggest that predation risk is an important factor determining nutrient excretion variation, warranting further attention. Our results also show that tissue nutrients and excretion nutrients are decoupled.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".