Host migration strategy is shaped by forms of parasite transmission and infection cost
Bibliographic record
Abstract
Most studies on the evolution of migration focus on food, mates and/or climate as factors influencing these movements, whereas negative species interactions such as predators, parasites and pathogens are often ignored. Although infection and its associated costs clearly have the potential to influence migration, thoroughly studying these interactions is challenging without a solid theoretical framework from which to develop testable predictions in natural systems. Here, we aim to understand when parasites favour the evolution of migration. We develop a general model which enables us to explore a broad range of biological conditions and to capture population and infection dynamics over both ecological and evolutionary time-scales. We show that when migration evolves depends on whether the costs of migration and infection are paid in reduced fecundity or survival. Also important are the parasite transmission mode and spatiotemporal dynamics of infection and recovery (if it occurs). Finally, we find that partial migration (where only a fraction of the population migrates) can evolve but only when parasite transmission is density-dependent. Our results highlight the critical, if overlooked, role of parasites in shaping long-distance movement patterns, and suggest that infection should be considered alongside more traditional drivers of migration in both empirical and theoretical studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".