Fear and food: Effects of predator‐derived chemical cues and stoichiometric food quality on<i>Daphnia</i>
Bibliographic record
Abstract
Abstract While resource quality and predator‐derived chemical cues can each have profound effects on zooplankton populations and their function in ecosystems, the strength and direction of their interactive effects remain unclear. We conducted laboratory experiments to evaluate how stoichiometric food quality (i.e., algal carbon [C] : phosphorus [P] ratios) affects responses of the zooplankter,Daphnia pulicaria,to predator‐derived chemical cues. We compared growth rates, body P content, metabolic rates, life‐history shifts, and survival of differentially P‐nourishedDaphniain the presence and absence of chemical cues derived from fish predators. We found effects of predator cues and/or stoichiometric food quality on all measured traits ofDaphnia. Exposure to fish cues led to reduced growth and increased metabolic rates but had little effect on the body %P content ofDaphnia. Elevated algal C : P ratios reduced growth and body %P and increased mass‐specific respiration rates. While most of the effects of predator cues and algal C : P ratios ofDaphniawere non‐interactive, reduced survival and relatedly reduced population growth rates that resulted from P‐poor food were amplified in the presence of predator‐derived cues. Our results demonstrate that stoichiometric food quality interacts with antipredator responses ofDaphnia, but these effects are largely trait dependent and appear connected to animal life‐history evolution. Given the ubiquity of predators and P‐poor food in lake ecosystems, our results highlight the importance of the interactive responses of animals to predator cues and poor nutrition.
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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".