Experimental elimination of parasites in nature leads to the evolution of increased resistance in hosts
Bibliographic record
Abstract
A reduction in the strength of selection is expected to cause the evolution of reduced trait expression. Elimination of a parasite should thus cause the evolution of reduced resistance to that parasite. To test this prediction in nature, we studied the fourth- and eighth-generation descendants of guppies (Poecilia reticulata) introduced into four natural streams following experimental elimination of a common and deleterious parasite (Gyrodactylus spp.). After two generations of laboratory rearing to control for plasticity and maternal effects, we infected individual fish to assess their resistance to the parasite. Contrary to theoretical expectations, the introduced guppy populations had rapidly and repeatably evolved increased resistance to the now-absent parasite. This evolution was not owing to a resistance-tolerance trade-off, nor to differences in productivity among the sites. Instead, a leading candidate hypothesis is that the rapid life-history evolution typical in such introductions pleiotropically increases parasite resistance. Our study adds a new dimension to the growing evidence for contemporary evolution in the wild, and also points to the need for a re-consideration of simple expectations from host-parasite theory. In particular, our results highlight the need for increased consideration of multiple sources of selection and pleiotropy when studying evolution in natural contexts.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".