Scared to evolve? Non-consumptive effects drive rapid adaptive evolution in a natural prey population
Bibliographic record
Abstract
Predators can strongly influence prey populations through both consumptive and non-consumptive effects. Nevertheless, most studies have focused on the consumptive effects in driving evolutionary changes. By integrating experimental evolution and resurrection ecology, we tested the roles of non-consumptive and consumptive effects in driving evolution in a Daphnia magna population that experienced strong changes in fish predation pressure. All resurrected genotypes were pooled, inoculated in outdoor mesocosms, and exposed to free-fish or caged-fish treatments. Non-consumptive effects induced rapid, repeatable changes in the clonal composition and associated genotypic trait changes that were similar in magnitude and direction to those imposed by killing. Both non-consumptive and consumptive effects caused a shift towards a dominance of the high-fish period clones that can perform better under fish predation, and this may be explained by the higher intrinsic growth rate of the high-fish period clones under predation risk. The genotypic trait changes (e.g. reduced body sizes, earlier maturation, more and smaller offspring) of the Daphnia in the mesocosm experiments were in the same direction as the adaptive trait shifts observed in situ through resurrection ecology. Our results demonstrate that non-consumptive effects can induce rapid adaptive evolution and may represent an overlooked driver of eco-evolutionary dynamics.
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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".