Global Amphibian Extinction Risk Assessment for the Panzootic Chytrid Fungus

By D. Rödder, J. Kielgast, J. Bielby, Sebastian Schmidtlein, J. Bosch, T. Garner, Michael Veith, S. Walker, Matthew Fisher, S. Lötters. 2009

180 citations recorded by the source corpus.

Species are being lost at increasing rates due to anthropogenic effects, leading to the recognition that we are witnessing the onset of a sixth mass extinction. Emerging infectious disease has been shown to increase species loss and any attempts to reduce extinction rates need to squarely confront this challenge. Here, we develop a procedure for identifying amphibian species that are most at risk from the effects of chytridiomycosis by combining spatial analyses of key host life-history variables with the pathogen's predicted distribution. We apply our rule set to the known global diversity of amphibians in order to prioritize pecies that are most at risk of loss from disease emergence. This risk assessment shows where limited conservation funds are best deployed in order to prevent further loss of species by enabling ex situ amphibian salvage operations and focusing any potential disease mitigation projects.

Subfields: Ecological Modeling, Global and Planetary Change, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e09c92748dafe3d5e4c2884adca9cee95b8032a5

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Citations in the corpus

Direct and Indirect Effects of Climate Change on Amphibian Populations (2010, background): “In amphibians, infectious diseases have been implicated in numerous population declines [7,165-167].”
Emerging threats and persistent conservation challenges for freshwater biodiversity (2018, background): “The global spread of chytridiomycosis caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd) has been linked to the extirpation or extinction of 200 species of frogs and toads (R¨odder et al. , 2009).”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, background): “This bias can be circumvented by sampling one point per cluster in environmental space [53,68,69].”