Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health

  1. Renata L. Muylaert
  2. Tigga Kingston
  3. Jinhong Luo
  4. Maurício Humberto Vancine
  5. Nikolas Galli
  6. Colin J. Carlson
  7. Reju Sam John
  8. Maria Cristina Rulli
  9. David T. S. Hayman

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Abstract

Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus ), given that several closely related viruses have been discovered and sarbecovirus–host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.

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  1. Version published to 10.1098/rspb.2022.0397
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    SciScore for 10.1101/2021.12.09.471691: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Code is provided in a GitHub repository (https://github.com/renatamuy/dynamic) and data in Zenodo (TBD, https://drive.google.com/drive/folders/1kBAi4eIGYRLXF7HLBc7QjpqyH4mHBEuu?usp=sharing).
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)
    Climate predictors were downloaded from WorldClim 2.1 [47] with 10 arc-minutes spatial resolution.
    WorldClim
    suggested: (WorldClim, RRID:SCR_010244)

    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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  3. Version published to 10.1101/2021.12.09.471691v1 on bioRxiv