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Experimental evidence of a pathogen invasion threshold
Author(s) -
Tad Dallas,
Martin Krkošek,
John M. Drake
Publication year - 2018
Publication title -
royal society open science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 51
ISSN - 2054-5703
DOI - 10.1098/rsos.171975
Subject(s) - pathogen , biology , transmission (telecommunications) , host (biology) , wildlife , ecology , replicate , microcosm , daphnia , range (aeronautics) , zoology , zooplankton , microbiology and biotechnology , statistics , telecommunications , materials science , mathematics , computer science , composite material
Host density thresholds to pathogen invasion separate regions of parameter space corresponding to endemic and disease-free states. The host density threshold is a central concept in theoretical epidemiology and a common target of human and wildlife disease control programmes, but there is mixed evidence supporting the existence of thresholds, especially in wildlife populations or for pathogens with complex transmission modes (e.g. environmental transmission). Here, we demonstrate the existence of a host density threshold for an environmentally transmitted pathogen by combining an epidemiological model with a microcosm experiment. Experimental epidemics consisted of replicate populations of naive crustacean zooplankton ( Daphnia dentifera ) hosts across a range of host densities (20–640 hosts l −1 ) that were exposed to an environmentally transmitted fungal pathogen ( Metschnikowia bicuspidata ). Epidemiological model simulations, parametrized independently of the experiment, qualitatively predicted experimental pathogen invasion thresholds. Variability in parameter estimates did not strongly influence outcomes, though systematic changes to key parameters have the potential to shift pathogen invasion thresholds. In summary, we provide one of the first clear experimental demonstrations of pathogen invasion thresholds in a replicated experimental system, and provide evidence that such thresholds may be predictable using independently constructed epidemiological models.

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