Factors affecting the expression of inducible defences in <i>Euplotes</i>: genotype, predator density and experience
Bibliographic record
Abstract
Summary Inducible defences alter the strength of interaction in food webs. Their effectiveness depends both on the maximum level of induction and the speed at which induction happens. Maximum level and speed of induction should therefore evolve in concert. We examined the effect of genotype, number of predators and previous exposure to predators on speed and maximum level of induction in a morphological defence expressed by eight clones in three species of the ciliate Euplotes. Both speed and maximum level of induction, and the reaction to predator density varied among genotypes. These results show that there is genetic variance for all aspects of this inducible defence and the potential for complex evolutionary change under selection. Higher predator densities led to higher maximum levels of defence and, for one measure of induction speed, more rapid induction. Previous exposure to predators had no detectable effect on either speed or maximum level of induction. Our results demonstrate that Euplotes can precisely and rapidly adjust their morphological defence to the magnitude of predation risk. The speed of induction and the maximum level of defence varied among genotypes and this will lead to variation in defence level and vulnerability under natural conditions. Variation in prey vulnerability is a key factor promoting stability in food webs.
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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 | 0.000 | 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".