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Erschienen in: EcoHealth 1/2017

07.04.2016 | Original Contribution

A Model to Inform Management Actions as a Response to Chytridiomycosis-Associated Decline

verfasst von: Sarah J. Converse, Larissa L. Bailey, Brittany A. Mosher, W. Chris Funk, Brian D. Gerber, Erin Muths

Erschienen in: EcoHealth | Sonderheft 1/2017

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Abstract

Decision-analytic models provide forecasts of how systems of interest will respond to management. These models can be parameterized using empirical data, but sometimes require information elicited from experts. When evaluating the effects of disease in species translocation programs, expert judgment is likely to play a role because complete empirical information will rarely be available. We illustrate development of a decision-analytic model built to inform decision-making regarding translocations and other management actions for the boreal toad (Anaxyrus boreas boreas), a species with declines linked to chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd). Using the model, we explored the management implications of major uncertainties in this system, including whether there is a genetic basis for resistance to pathogenic infection by Bd, how translocation can best be implemented, and the effectiveness of efforts to reduce the spread of Bd. Our modeling exercise suggested that while selection for resistance to pathogenic infection by Bd could increase numbers of sites occupied by toads, and translocations could increase the rate of toad recovery, efforts to reduce the spread of Bd may have little effect. We emphasize the need to continue developing and parameterizing models necessary to assess management actions for combating chytridiomycosis-associated declines.
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Metadaten
Titel
A Model to Inform Management Actions as a Response to Chytridiomycosis-Associated Decline
verfasst von
Sarah J. Converse
Larissa L. Bailey
Brittany A. Mosher
W. Chris Funk
Brian D. Gerber
Erin Muths
Publikationsdatum
07.04.2016
Verlag
Springer US
Erschienen in
EcoHealth / Ausgabe Sonderheft 1/2017
Print ISSN: 1612-9202
Elektronische ISSN: 1612-9210
DOI
https://doi.org/10.1007/s10393-016-1117-9

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