Parasites can cause selection against migrants following dispersal between environments
Bibliographic record
Abstract
Summary 1. The potential for selection against migrants to promote population divergence and speciation is well established in theory, yet there has been relatively little empirical work that has explicitly considered selection against migrants as a form of reproductive barrier, and its importance in the accumulation of reproductive isolation between populations has been overlooked until recently. 2. Parasites often differ between environments and can be an important source of selection on hosts, yet their contribution to population divergence in general, and selection against migrants in particular, is poorly understood. 3. Selection against migrants might be reduced if organisms escape parasitism when they disperse (natural enemy release). Alternatively, parasites could increase selection against migrants if, when they disperse, organisms encounter parasites to which they are poorly adapted. 4. Here we test experimentally the contribution that parasites could make to selection against migrants in the adaptive radiation of three‐spined sticklebacks. These fish have repeatedly colonized freshwater environments from marine ones, and this has repeatedly lead to rapid speciation. 5. We use transplant experiments of lab‐raised fish to simulate dispersal, and antihelminthic treatment to show that ancestral‐type marine sticklebacks contract higher burdens of novel parasites when introduced to freshwater, than in saltwater, and suffer a growth cost as a direct result. 6. Susceptibility to parasites and their detrimental effect is less in derived, freshwater fish from evolutionarily young populations, possibly as a result of selection for resistance. 7. Our results support a role for parasites in selection against migrants and population diversification. They do not support the hypothesis of ‘natural enemy release’.
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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) | 0.004 | 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".