• Amphibian chytrid fungus;
  • Batrachochytrium dendrobatidis;
  • Infection Threshold;
  • Host Species


Introduced pathogens are increasingly being implicated in population declines and their effects are difficult to manage. In the absence of methods to eradicate pathogens acting as threatening processes, intervention before population decline is necessary. Such an intervention requires an ability to predict when population declines will occur, and therefore, an understanding of when exposure will lead to infection, disease, death and population decline. This study investigates when pathogen exposure leads to disease for the amphibian chytrid fungus Batrachochytrium dendrobatidis, which has been implicated as a causal agent in the global amphibian decline. Susceptibility studies were conducted on two anuran species, the green and golden bell frog Litoria aurea and the striped marsh frog Limnodynastes peronii, when exposed to the fungus as either tadpoles or juveniles. Host species was found to significantly affect the outcome of exposure, with infection loads in L. aurea increasing over time and resulting in significantly lower survival rates than unexposed. By comparison, infection loads in L. peronii remained the same or decreased over time following the initial infection, and survival rates were no different whether exposed to B. dendrobatidis or not. These outcomes were independent of the life stage at exposure. Individuals with higher infection loads were not found to have lower survival rates; rather, an infection load threshold was identified where individuals with infection loads that crossed this threshold had high likelihoods of showing terminal signs of chytridiomycosis. Therefore, host species determined whether infection load crossed this threshold and the crossing of the threshold determined the incidence of disease and survival. The quantification of infection load thresholds for survival, along with the time it takes to reach them, will enable infection loads in wild populations to be related to the likelihood of disease and is the first step in the understanding and prediction of when exposure will result in population decline.