An epi-evolutionary model to predict spore-producing pathogens adaptation to quantitative resistance in heterogeneous environments

  1. Frédéric Fabre
  2. Jean-Baptiste Burie
  3. Arnaud Ducrot
  4. Sébastien Lion
  5. Quentin Richard
  6. Ramsès Djidjou-Demasse

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Abstract

We model the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits describing its infection efficiency and a time-varying sporulation curve taking into account lesion ageing. We first derive a general expression of the basic reproduction number ℛ 0 for fungal pathogens in heterogeneous environments. We show that evolutionary attractors of the model coincide with local maxima of ℛ 0 only if the infection efficiency is the same on all host types. We then study how three basic resistance characteristics (pathogenicity trait targeted, resistance effectiveness, and adaptation cost) in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar) (i) lead to pathogen diversification at equilibrium and (ii) shape the transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance impacting only the sporulation curve will always lead to a monomorphic population, while dimorphism ( i . e . pathogen diversification) can occur with resistance altering infection efficiency, notably with high adaptation cost and proportion of R cultivar. Accordingly, the choice of quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the emergence time of the evolutionary attractor best adapted to the R cultivar tends to be shorter when the resistance impacts infection efficiency than when it impacts sporulation. In contrast, from an epidemiological perspective, the epidemiological control is always higher when the resistance impacts infection efficiency. This highlights the difficulty of defining deployment strategies of quantitative resistance maximising at the same time epidemiological and evolutionary outcomes.

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  1. Version published to 10.1101/423467v5 on bioRxiv
  2. Version published to 10.1111/eva.13328
  3. ScreenIT

    SciScore for 10.1101/423467: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  4. Version published to 10.1101/423467v4 on bioRxiv
  5. Version published to 10.1101/423467v3 on bioRxiv
  6. Version published to 10.1101/423467v2 on bioRxiv
  7. Version published to 10.1101/423467v1 on bioRxiv