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Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease

View ORCID ProfileKonstans Wells, Rodrigo K. Hamede, Menna E. Jones, Paul A. Hohenlohe, Andrew Storfer, Hamish I. McCallum
doi: https://doi.org/10.1101/392324
Konstans Wells
1Environmental Futures Research Institute, Griffith University, Brisbane QLD 4111, Australia
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  • For correspondence: konswells@gmail.com
Rodrigo K. Hamede
2School of Biological Sciences, University of Tasmania, Private Bag 55, Hobart, Tasmania 7001, Australia
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Menna E. Jones
2School of Biological Sciences, University of Tasmania, Private Bag 55, Hobart, Tasmania 7001, Australia
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Paul A. Hohenlohe
3Institute for Bioinformatics and Evolutionary Studies, Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA
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Andrew Storfer
4School of Biological Sciences, Washington State University, Pullman, WA 99164-4236 USA
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Hamish I. McCallum
1Environmental Futures Research Institute, Griffith University, Brisbane QLD 4111, Australia
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Abstract

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short-term epidemic properties, such as controlling early outbreaks. Predicting long-term endemic characteristics with limited retrospective data is more challenging. We used individual-based modelling informed by individual variation in pathogen load and transmissibility to predict long-term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed Approximate Bayesian Computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from ten years of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics. Only 21% of matching scenarios led to devil extinction within 100 years following devil facial tumour disease (DFTD) introduction, whereas DFTD faded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 years, usually with long-period oscillations. Our findings show that pathogen extirpation or host-pathogen coexistence are much more likely than the DFTD-induced devil extinction, with crucial management ramifications. Accounting for individual-level disease progression and the long-term outcome of devil-DFTD interactions at the population-level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long-term population declines at equilibria. Our modelling approach is widely applicable to other host-pathogen systems to predict disease impact beyond transient dynamics.

Footnotes

  • E-mail addresses (following order of authorship): konswells{at}gmail.com, Rodrigo.HamedeRoss{at}utas.edu.au, MennaJones{at}utas.edu.au, hohenlohe{at}uidaho.edu, astorfer{at}wsu.edu, h.mccallum{at}griffith.edu.au

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 15, 2018.
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Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease
Konstans Wells, Rodrigo K. Hamede, Menna E. Jones, Paul A. Hohenlohe, Andrew Storfer, Hamish I. McCallum
bioRxiv 392324; doi: https://doi.org/10.1101/392324
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Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease
Konstans Wells, Rodrigo K. Hamede, Menna E. Jones, Paul A. Hohenlohe, Andrew Storfer, Hamish I. McCallum
bioRxiv 392324; doi: https://doi.org/10.1101/392324

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