Underestimated ranges and overlooked refuges from amphibian chytridiomycosis

Authors

  • Robert Puschendorf,

    Corresponding author
    1. Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Qld 4811, Australia
    • School of Marine and Tropical Biology, James Cook University, Townsville, Qld 4811, Australia
    Search for more papers by this author
  • Lauren Hodgson,

    1. Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Qld 4811, Australia
    2. Division of Research and Innovation, James Cook University, Townsville, Qld 4811, Australia
    Search for more papers by this author
  • Ross A. Alford,

    1. School of Marine and Tropical Biology, James Cook University, Townsville, Qld 4811, Australia
    2. Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Qld 4811, Australia
    Search for more papers by this author
  • Lee F. Skerratt,

    1. School of Public Health, Tropical Medicine and Rehabilitation Sciences, James Cook University, Townsville, Qld 4811, Australia
    Search for more papers by this author
  • Jeremy VanDerWal

    1. Centre for Tropical Biodiversity and Climate Change, James Cook University, Townsville, Qld 4811, Australia
    2. Division of Research and Innovation, James Cook University, Townsville, Qld 4811, Australia
    Search for more papers by this author

Correspondence: Robert Puschendorf, School of Marine and Tropical Biology, James Cook University, Townsville, Qld 4811, Australia.

E-mails: robert.puschendorf@jcu.edu.au; robert.puschendorf@gmail.com

Abstract

Aim

Accurately documenting and predicting declines and shifts in species’ distributions is fundamental for implementing effective conservation strategies and directing future research; species distribution models (SDM) have become a powerful tool for such work. Nevertheless, much of the data used to create these models are opportunistic and often violate some of their basic assumptions. We use amphibian declines and extinctions linked to the fungus Batrachochytrium dendrobatidis (Bd) to examine how sampling biases in data collection can affect what we know of this disease and its effect on amphibians in the wild.

Location

Queensland, Australia.

Methods

We developed a distribution model for Bd incorporating known locality records for Bd and a subset of climatic variables that should correctly characterize its distribution. We tested this (original) model with additional surveys, recorded new Bd observations in novel environments and reran the distribution model. We then investigated the difference between the original and new models, and used frog abundance and infection status data from two of these new localities to look at the susceptibility of the torrent frog Litoria nannotis to chytridiomycosis.

Results

While largely correct, the original SDM underestimated the distribution of Bd; sampling in ‘unsuitable’ drier environments discovered abundant populations of susceptible frogs with pathogen prevalences of up to 100%. The validation surveys further uncovered a new population of the frog Litoria lorica coexisting with the pathogen; this species was previously believed to be an extinct rain forest endemic.

Main conclusion

Our results indicate that SDMs constructed using opportunistically collected data can be biased if species are not at equilibrium with their environment or because environmental gradients have not been adequately sampled. For disease ecology, the better estimations of pathogen distribution may lead to the discovery of new populations persisting at the edge of their range, which has important implications for the conservation of species threatened by chytridiomycosis.

Ancillary