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PreprintCC BYbioRxiv

Epidemic size and host-parasite (co)-evolution: a meta-analysis

  1. 1University of Stirling

Abstract

The size of an epidemic can influence the strength of selection acting on hosts and parasites, but whether epidemic size consistently shapes host-parasite (co)-evolution remains unclear. I conducted a meta-analysis of studies that simultaneously quantified epidemic size and evolutionary change in hosts, parasites or their interactions, synthesising 123 effect sizes from 11 studies. I tested whether epidemic size was associated with four components of evolutionary change: host evolution, parasite evolution, non-additive host-parasite coevolution and net coevolution. Larger epidemics were associated with greater host evolutionary change in invertebrates, whereas the relationship was positive in plants and negative but non-significant in bacteria. Parasite evolution, non-additive coevolution and net coevolution were not detectably associated with epidemic size, although parasite infectivity showed a relatively consistent increase across studies. These results provide limited evidence for a general relationship between epidemic size and host-parasite (co)-evolution and instead suggest that evolutionary responses depend on the host-parasite system and broader ecological and temporal context. Interpretation is constrained by the small and taxonomically biased evidence base, particularly the strong representation of Daphnia, and by substantial heterogeneity in how epidemic size and evolutionary change were measured and defined. More studies across diverse host-parasite systems are required that track both evolutionary change and disease prevalence through time. This work also provides an empirical basis for incorporating coevolutionary feedbacks into quantitative epidemiological models.

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BibTeX
@misc{paplauskas2026epidemic,
  title = {{Epidemic size and host-parasite (co)-evolution: a meta-analysis}},
  author = {S. Paplauskas},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.18.752591},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.18.752591v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Epidemic size and host-parasite (co)-evolution: a meta-analysis
AU  - S. Paplauskas
PY  - 2026
DA  - 2026/09/18
PB  - bioRxiv
DO  - 10.64898/2026.09.18.752591
UR  - https://www.biorxiv.org/content/10.64898/2026.09.18.752591v1
AB  - The size of an epidemic can influence the strength of selection acting on hosts and parasites, but whether epidemic size consistently shapes host-parasite (co)-evolution remains unclear. I conducted a meta-analysis of studies that simultaneously quantified epidemic size and evolutionary change in hosts, parasites or their interactions, synthesising 123 effect sizes from 11 studies. I tested whether epidemic size was associated with four components of evolutionary change: host evolution, parasite evolution, non-additive host-parasite coevolution and net coevolution. Larger epidemics were associated with greater host evolutionary change in invertebrates, whereas the relationship was positive in plants and negative but non-significant in bacteria. Parasite evolution, non-additive coevolution and net coevolution were not detectably associated with epidemic size, although parasite infectivity showed a relatively consistent increase across studies. These results provide limited evidence for a general relationship between epidemic size and host-parasite (co)-evolution and instead suggest that evolutionary responses depend on the host-parasite system and broader ecological and temporal context. Interpretation is constrained by the small and taxonomically biased evidence base, particularly the strong representation of Daphnia, and by substantial heterogeneity in how epidemic size and evolutionary change were measured and defined. More studies across diverse host-parasite systems are required that track both evolutionary change and disease prevalence through time. This work also provides an empirical basis for incorporating coevolutionary feedbacks into quantitative epidemiological models.
ER  -