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Data from: Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations

Citation

Auld, Stuart K.J.R.; Brand, June; Auld, Stuart K. J. R. (2017), Data from: Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations, Dryad, Dataset, https://doi.org/10.5061/dryad.3r37c

Abstract

Epidemics are engines for host-parasite coevolution, where parasite adaptation to hosts drives reciprocal adaptation in host populations. A key challenge is to understand whether parasite adaptation and any underlying evolution and coevolution is repeatable across ecologically realistic populations that experience different environmental conditions, or if each population follows a completely unique evolutionary path. We established twenty replicate pond populations comprising an identical suite of genotypes of crustacean host, Daphnia magna, and inoculum of their parasite, Pasteuria ramosa. Using a time-shift experiment, we compared parasite infection traits before and after epidemics and linked patterns of parasite evolution with shifts in host genotype frequencies. Parasite adaptation to the sympatric suite of host genotypes came at a cost of poorer performance on foreign genotypes across populations and environments. However, this consistent pattern of parasite adaptation was driven by different types of frequency-dependent selection that was contingent on an ecologically relevant environmental treatment (whether or not there was physical mixing of water within ponds). In unmixed ponds, large epidemics drove rapid and strong host-parasite coevolution. In mixed ponds, epidemics were smaller and host evolution was driven mainly by the mixing treatment itself; here, host evolution and parasite evolution were clear, but coevolution was absent. Population mixing breaks an otherwise robust coevolutionary cycle. These findings advance our understanding of the repeatability of (co)evolution across noisy, ecologically realistic populations.

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