Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Article Dans Une Revue Scientific Reports Année : 2018

Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management

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Infectious diseases raise many concerns for wildlife and new insights must be gained to manage infected populations. Wild ungulates provide opportunities to gain such insights as they host many pathogens. Using modelling and data collected from an intensively monitored population of Pyrenean chamois, we investigated the role of stochastic processes in governing epidemiological patterns of pestivirus spread in both protected and hunted populations. We showed that demographic stochasticity led to three epidemiological outcomes: early infection fade-out, epidemic outbreaks with population collapse, either followed by virus extinction or by endemic situations. Without re-introduction, the virus faded out in >50% of replications within 4 years and did not persist >20 years. Test-and-cull of infected animals and vaccination had limited effects relative to the efforts devoted, especially in hunted populations in which only quota reduction somewhat improve population recovery. Success of these strategies also relied on the maintenance of a high level of surveillance of hunter-harvested animals. Our findings suggested that, while surveillance and maintenance of population levels at intermediate densities to avoid large epidemics are useful at any time, a ‘do nothing’ approach during epidemics could be the ‘least bad’ management strategy in populations of ungulates species facing pestivirus infection.
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hal-02493521 , version 1 (26-05-2020)

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Sébastien Lambert, Pauline Ezanno, Mathieu Garel, Emmanuelle Gilot-Fromont. Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management. Scientific Reports, 2018, 8 (1), ⟨10.1038/s41598-018-34623-0⟩. ⟨hal-02493521⟩
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