Inducible defences are a stabilizing factor for predator and prey populations: a field experiment
Bibliographic record
Abstract
Summary 1. Based on mathematical models, antipredator defence mechanisms are commonly believed to have stabilizing effects on communities. However, empirical data are still lacking. 2. We tested stabilizing effects of an inducible vertical migration defence in two Daphnia pulex clones in a 5‐week field enclosure experiment. A defended (migrated down into darker water layers in the presence of fish chemicals in both laboratory and field experiments) and non‐defended (no ability to react to fish chemicals) clone were directly exposed to fish predators and compared to control enclosures (no fish). 3. In the absence of planktivorous fish, both defended and non‐defended clones exhibited boom‐and‐bust dynamics, probably owing to over‐exploitation of the food source. Predation almost led to extinction of the non‐defended Daphnia clone during the experiment and the fish, deprived of food, lost weight. However, the population density of the defended clone was stable and it did not over‐exploit the algal food source, while there was a continuous supply of food to the fish, which consequently gained weight. 4. We conclude that both consumptive and non‐consumptive (also called non‐lethal or trait‐mediated) predator effects, coupled with prey defences, are key contributors to prey stability. This has a positive effect on both the predator and the food organism of the prey.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".