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Shoaling guppies evade predation but have deadlier parasites

Nature Ecology & Evolution volume 6, pages 945–954 (2022)Cite this article

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Abstract

Parasites exploit hosts to replicate and transmit, but overexploitation kills both host and parasite. Predators may shift this cost–benefit balance by consuming infected hosts or changing host behaviour, but the strength of these effects remains unclear. Here we use field and lab data on Trinidadian guppies and their Gyrodactylus spp. parasites to show how differential predation pressure influences parasite virulence and transmission. We use an experimentally demonstrated virulence–transmission trade-off to parametrize a mathematical model in which host shoaling (as a means of anti-predator defence), increases contact rates and selects for higher virulence. Then we validate model predictions by collecting parasites from wild, Trinidadian populations; parasites from high-predation populations were more virulent in common gardens than those from low-predation populations. Broadly, our results indicate that reduced social contact selects against parasite virulence.

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Fig. 1: Predators alter selection on virulence through consumptive and non-consumptive pathways.
Fig. 2: Natural guppy populations differ in predation, driving evolutionary divergence in shoaling rate.
Fig. 3: Infection intensity links transmission rate and virulence with a stabilizing trade-off.
Fig. 4: Predation drives increased shoaling rate and virulence in the eco-coevolutionary model.
Fig. 5: Predation increases shoaling rate and thus selects for higher virulence.

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Data availability

The data is available at https://doi.org/10.5061/dryad.k3j9kd59h.

Code availability

The code is available at https://doi.org/10.5061/dryad.k3j9kd59h.

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Acknowledgements

We thank M. Ramlal, D. Reznick and E. Rudzki for assistance with fieldwork. E. Calcaterra, L. Colgan, J. Jokela, M. Sackett and N. Tardent provided technical assistance with parasite genotyping. J. Jokela, A. McKay, C. van Oosterhout, M. Turcotte, K. A. Young and three anonymous reviewers made useful comments on an earlier version of this manuscript. National Science Foundation Division of Environmental Biology number 2010826 (J.C.W.), National Science Foundation Division of Environmental Biology number 2010741 (M.J.J.), National Science Foundation Division of Graduate Education number 1747452 (F.R.) and University of Pittsburgh Central Research Development Fund (J.F.S.) provided funding.

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Authors and Affiliations

  1. Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Jason C. Walsman, Mary J. Janecka, David R. Clark, Rachael D. Kramp, Faith Rovenolt, Regina Patrick & Jessica F. Stephenson

  2. Department of Life Sciences, University of the West Indies, St. Augustine, Trinidad and Tobago

    Ryan S. Mohammed

  3. Biology Department, Thompson Biology Lab, Williams College, Williamstown, MA, USA

    Ryan S. Mohammed

  4. Evolutionary Biology Group, Faculty of Biology, Adam Mickiewicz University, Poznań, Poland

    Mateusz Konczal

  5. School of Biological Sciences, University of Nebraska, Lincoln, NE, USA

    Clayton E. Cressler

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Contributions

Conceptualization: J.F.S. and J.C.W. Theoretical modelling: J.C.W., C.E.C. and J.F.S. Data collection from literature: F.R. Sensitivity analysis: J.C.W. and F.R. Field collections: M.J.J., D.R.C., R.P. and R.S.M. Laboratory trait measurements: R.P., D.R.C., M.J.J., R.D.K. and J.F.S. Parasite molecular work: M.J.J., R.D.K. and M.K. Parasite genetic analysis: M.J.J. and M.K. Trait data analysis: J.C.W., D.R.C. and J.F.S. Density and prevalence data analysis: J.C.W. Funding acquisition: J.C.W., M.J.J., F.R. and J.F.S. Writing, original draft: J.C.W. and J.F.S. Writing, review and editing: J.C.W., M.J.J., D.R.C., R.D.K., F.R., R.P., R.S.M., M.K., C.E.C. and J.F.S.

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Correspondence to Jason C. Walsman.

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Extended data

Extended Data Fig. 1 Flow of information between empirical results (row 1), data uses (row 2), and theory (row 3).

(A) Much of the training data came from our field surveys, previously published surveys, and previously published laboratory data (see Fig. 5f–h for field surveys and see Table 1). (B) With our transmission rate experiment (and previously published data), we established the relationship between intensity and transmission rate (Fig. 3a). We connected these data to those from our line traits experiment, which link intensity and death rate (C; a measure of virulence shown in Fig. 3b). Together, these parameterize the transmission and virulence trade-off as training data (Fig. 3c). (D) The line traits experiment also provided validating data (Fig. 5i,j) on the average virulence and intensity of our four wild populations. (E) All training data, including the trade-off, was used to fit the theoretical model. (F) Once fit, the eco-coevolutionary model predicts where along the trade-off parasites should evolve (Figs. 4, 5d,e), predicting average virulence in the four populations of the validating data. Created with Biorender.com.

Extended Data Fig. 2 Data from field survey of coinfection rates in the wild.

For each site (river + predation regime), we genotyped a subset of worms from a sample of fish hosting more than one worm. We show the percent of infections that were pure (of either parasite species, light-yellow columns) and the total percent of pure infections (mid-blue column is sum of light-yellow columns). We also show the percent of coinfections that were multi-genotype coinfections of one species, the other, or contained both species (light pink columns). The sum of just the light pink columns gives the total rate of coinfection for fish infected with more than 1 worm (dark-purple column). The mid-blue and dark-purple columns must always sum to 100% in every row. We multiply the total coinfection rate (dark-purple; coinfections/infections with > 1) by the percentage of infections that have more than 1 worm to get the final, adjusted coinfection rate (coinfections/infections). See Supplementary Fig. 5 for a graphical example. G.t. = G. turnbulli and G.b. = G. bullatarudis.

Extended Data Fig. 3 Neither selective predation nor variation in host immunity qualitatively alter key model outcomes.

We compared the default model case (squares) to variations with selective predation (circles) or immune variation (triangles). We also compare outcomes with full coevolution at a given predation level (colour; P corresponding to Fig. 5) to outcomes without host evolution (grey). (A) Selective predation led to coevolution of lower shoaling rate. Increased immunity in low-predation populations led to coevolution of somewhat higher shoaling rate while decreased immunity in high-predation populations led to coevolution of somewhat lower shoaling rate. (B) Selective predation led to coevolution of lower virulence. Increased immunity in low-predation populations led to coevolution of higher virulence while decreased immunity in high-predation populations led to coevolution of lower virulence. For all models, increased virulence was driven by increased shoaling rate (compare colour points to grey). (C) Selective predation led to lower coevolutionary prevalence. Increased immunity led to lower coevolutionary prevalence while decreased immunity led to higher. (D) Selective predation led to higher coevolutionary host density. Increased immunity led to higher coevolutionary host density while decreased host density led to lower coevolutionary host density.

Extended Data Fig. 4 Virulence can select for increased shoaling rate.

Hosts can evolve increasing shoaling rate in response to increased virulence, especially at very high virulence (beyond range used in main text). (A) Increasing virulence (and transmissibility along the trade-off) can decrease prevalence. (B) Overall parasite-induced mortality can decrease if prevalence declines sharply enough. This decrease occurs because, while parasites are very virulent, very few hosts are infected and suffering that virulence. (C) At high virulence, increasing virulence can select for higher host shoaling rates. Parameters used: c = 2 used for (A) and (B); P = 0.074 used for (C). All other parameters at default (Table 1).

Extended Data Fig. 5 Host evolution in response to increasing predation causes parasite-induced mortality (red curves) to increase more than predator-induced mortality (black curves).

(A) Without host evolution (shoaling rate, c, set to the green point in Fig. 4 while parasites evolve to some CSS), parasite-induced mortality declines with predation while predator-induced mortality increases. Death from background sources (d, grey line) does not change. (B) This trend is similar for a higher c (set to high, blue point in Fig. 4). (C) When hosts evolve increasing c with increasing predation (coCSS curve connecting green and blue points in Fig. 4), parasite-induced mortality increases more than predator-induced mortality. This pattern is due to host evolution and is qualitatively unchanged if hosts evolve but parasites do not.

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Walsman, J.C., Janecka, M.J., Clark, D.R. et al. Shoaling guppies evade predation but have deadlier parasites. Nat Ecol Evol 6, 945–954 (2022). https://doi.org/10.1038/s41559-022-01772-5

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